Methods for identifying subjects with a genetic risk for developing IgA nephropathy

ABSTRACT

Seven protective alleles for IgA nephropathy have been discovered that can be identified by analyzing a DNA sample for seven respective SNPs. A method is provided for identifying and treating subjects at risk of developing IgA neuropathy based on a new seven-SNP genetic risk score. Also provided are screening methods to identify compounds that bind to and reduce the expression or biological activity of a either CFHR1 or CFHR3.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a 371 national stage entry application ofPCT/US12/025742, filed on Feb. 17, 2012, and claims benefit ofProvisional Appln. 61/444,583, filed Feb. 18, 2011, and ProvisionalAppln. 61/444,126, filed Feb. 17, 2011, the entire contents of which arehereby incorporated by reference as if fully set forth herein, under 35U.S.C. §119(e).

STATEMENT OF GOVERNMENT INTEREST

The invention was made with Government support under DK087445 andDK082753 awarded by the National Institutes of Health. The Governmenthas certain rights in the invention.

FIELD OF THE INVENTION

This in invention is in the field of genetic risk factors for kidneydisease, specifically IgA nephropathy (IgAN).

BACKGROUND

Chronic kidney disease is a major cause of morbidity and mortalityaffecting 10-20% of the world population, with glomerulonephritisaccounting for a significant proportion of cases¹⁻³. IgA nephropathy(IgAN) is the most common form of glomerulonephritis and the most commoncause of kidney failure among Asian populations^(2,4). The diagnosis ofIgAN requires documentation by kidney biopsy demonstrating proliferationof the glomerular mesangium with deposition of immune complexespredominantly composed of Immunoglobulin A (IgA) and complement C3proteins^(3,5,6). Registry data as well as autopsy and kidney-donorbiopsy series suggest that there is a significant variation inprevalence among different ethnicities: IgAN is most frequent amongAsians, with a disease prevalence as high as 3.7% detected amongJapanese kidney donors, but is rare among individuals of Africanancestry⁵ and of intermediate prevalence among Europeans (up to 1.3%)⁶.

The pathogenesis of IgAN is uncertain^(8,9). The finding of IgA1glycosylation abnormalities among European, Asian, and African-Americanpopulations has suggested a shared pathogenesis among differentgroups¹⁰⁻¹⁵. Moreover, familial aggregation of IgAN has been reportedamong all ethnicities, suggesting a genetic component to disease^(8,16).To date linkage studies have identified several loci predisposing toIgAN, but underlying genes are not known^(8,16-18). A single,unreplicated genome-wide association study (GWAS) in a small Europeancohort (533 cases) has reported association of IgAN with the MHCcomplex¹⁹.

Identifying specific mutations in one or more genes could be used as thebasis for a noninvasive method to diagnose a predisposition to IgAN, anddeciding which indications merit undergoing renal biopsy andprophylactic treatment.

SUMMARY OF THE INVENTION

Seven new protective alleles associated with identified SNPs have beenidentified that reduce a subject's risk of developing IgA nephropathy.Certain embodiments are directed to methods for determining if a subjecthas one or more protective alleles and hence a reduced risk of IgAN.This is accomplished by a. obtaining a DNA sample from the subject, b.analyzing the DNA sample to detect the presence of one or more SNPsselected from the group comprising [either rs6677604 or rs3766404],rs9275596, rs9275224, rs2856717, [either rs9357155 or rs2071543],[either rs1883414 or rs3129269] and [either rs2412971 or rs2412973], c.determining whether the sample has one or more SNPs, wherein each of theSNPs indicates a respective protective allele, and d. determining thatthe subject has a reduced risk of developing IgA nephropathy if thesubject has at least one protective allele. The method above can furtherinclude calculating a genetic risk score comprising determining aweighted sum of the number of protected alleles in the DNA sample,multiplied by the log of the odds ratio for each of the individualprotected alleles.

Another set of embodiments is directed to a method for determiningwhether or not to treat a subject by analyzing the DNA sample to detectthe presence of one or more SNPs selected from the group comprising[either rs6677604 or rs3766404], rs9275596, rs9275224, rs2856717,[either rs9357155 or rs2071543], [either rs1883414 or rs3129269] and[either rs2412971 or rs2412973], determining whether the sample has oneor more SNPs, wherein each of the SNPs indicates a respective protectiveallele, and treating the subject for IgAN if the subject does not haveat least one protective allele. In an embodiment the treatment includesadministering therapeutically effective amounts of one or more steroids.

Another set of embodiments is directed to methods for screeningcandidate compounds in a library to identify compounds that bind toCFHR1 and CFHR3 (target proteins); then providing the target protein;contacting the candidate compounds with the target protein underconditions suitable for binding of the compounds to the protein,screening the library of candidate compounds for a compound that hashigh affinity binding to the target protein; and if a compound binds tothe target protein with high affinity, then determining if binding ofthe compound to the target protein reduces the biological activity ofthe target protein, and finally selecting the compound if it binds withhigh affinity to the target protein and thereby reduces the biologicalactivity of the target protein. The compounds can be small molecules,peptides or antibodies and they are preferably bound to a solid support.

Another set of embodiments is directed to methods for treating orpreventing IgAN in a subject by reducing the expression of CFHR1 orCFHR3, or both comprising administering therapeutically effectiveamounts of inhibitory oligonucleotides that reduce the expression ofCFHR1 or CFHR3, or both.

Other embodiments are directed to microarrays comprising two or moreoligonucleotides bound to a support that are complementary to andhybridize to one or more respective target oligonucleotide selected fromthe group comprising (i) rs6677604 (A), rs9275596 (C), rs9357155 (A),rs1883414 (T), rs2412971(A), rs9275224 (A) and rs2856717 (T); or (ii)rs3766404 (C), rs2856717 (T), rs2071543 (A), rs3129269 (T), rs2412973(A), rs9275224 (A) and rs2856717 (T), wherein each of the SNPs indicatesa respective protective allele. Preferably. the oligonucleotides boundto the support are complementary to and hybridize with the targetoligonucleotides in the group consisting of [either rs6677604 orrs3766404], rs9275596, rs9275224, rs2856717, [either rs9357155 orrs2071543], [either rs1883414 or rs3129269] and [either rs2412971 orrs2412973].

These and other features, aspects, and advantages of the presentinvention will become better understood with regard to the followingdescription, appended claims, and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. High resolution view of the MHC locus. The X-axis representsphysical distance (kb). The left Y-axis represent the −log(p-values) forthe association statistics. The −log(p-values) in the discovery andcombined cohorts are shown as blue circles and red diamonds,respectively. The right Y-axis represents the average recombinationrates based on the phased HapMap haplotypes. The recombination rates areshown by the light blue line (a) The three intervals associated with IgAnephropathy reside within a 0.54 Mb segment on chromosome 6. The shadedareas correspond to regional plots in lower panels; (b) Regional plotfor the interval containing HLA-DQB1, DQA1, and DRB1. The classical HLAalleles imputed in the discovery cohort (green triangles) formed aprotective haplotype DQB1*0602-DQA1*0102-DRB1*1501. (c) Regional plotfor the second MHC interval: SNPs typed in the combined cohorts residewithin the PSMB8 gene. (d) Regional plot for the HLA-DPB2, DPB1, andDPA1 interval. The lower panels for (b-d) represent linkagedisequilibrium (LD) heatmaps (D′) calculated based on the actualgenotype data of the Beijing cohort.

FIG. 2 Analysis of the Chr. 1 and Chr 22. loci. (a) Regional associationplot of the chromosome 1q32 locus; while the most strongly associatedSNP resides within the CFH gene, it is a perfect proxy for CFHR1,3Δ. Thelower panel represents the LD heatmap (D′) calculated based on thegenotype data of the Beijing cohort. (b) Haplotype FD analysis revealedfive common haplotypes (H-1 to H-5) in the Beijing discovery cohort(freq.>0.01). The haplotype frequencies, corresponding tag-SNPs andreported disease associations are shown^(22-24,36,37,41,43). The H2haplotype perfectly tags CFHR1,3Δ. The odds ratios (ORs) and 95%confidence intervals (95% CIs) are calculated in reference to H-1, whichhas an identical frequency among cases and controls. *** p=7.7×10⁻⁶ forcomparison of H-2 versus all other haplotypes. (c) Regional associationplot of the chromosome 22 locus: the strongest association stems fromthe SNPs residing within HORMAD2, but the area of association spans over˜0.7 Mb region containing multiple genes.

FIG. 3 . . . Multiplicative interaction between Chr. 22q12 (rs2412971)and Chr. 1q32 (rs6677604) loci. The allelic effects of rs2412971-A bygenotype class of rs9275596 (top signal in the HLA, no interaction) andrs6677604 (top signal in at CFHR1/R3 locus on Chr. 1q32, significantinteraction). The protective effect of rs2412971-A allele is reversed inhomozygotes for the rs6677604-A allele, which tags a deletion inCFHR3/R1. Error bars correspond to 95% confidence intervals.

DETAILED DESCRIPTION

Seven protective alleles for IgA nephropathy have been discovered thatcan be identified by analyzing a DNA sample for seven respective SNPs.Certain embodiments of the present invention are directed to a method ofidentifying and treating subjects at risk of developing IgA nephropathybased on a new seven-SNP genetic risk score.

It was also discovered that certain protective alleles (rs6677604 andrs3766404) are located in a 100-kb segment on Chr. 1q31-q32.1 in intron12 of CFH and that one allele perfectly tags a common deletion spanningthe entire CFHR1 and CFHR3 genes (CFHR1,3Δ).^(22,23) This protectiveallele confers a two-fold protection in the development of IgAnephropathy in Asian and Caucasian population (p=1×10-9). Thereforeinhibiting the expression of the proteins CFHR1 or CFHR3 is the basis ofa therapy for treating or preventing IgAN, since none of the subjectswith these deletions had any detectable adverse side effects. Otherembodiments are directed to methods of treating IgAN by administeringtherapeutically effective amounts of inhibitory oligonucleotides thatreduce the expression of either CFHR1 or CFHR3 or both. In someembodiments microRNAs that target mRNA encoding either CFHR1 or CFHR3 orboth are administered therapeutically to treat IgAN. Other embodimentsare directed to screening methods to identify compounds that bind to andreduce the expression or biological activity of a either CFHR1 or CFHR3.

Summary of the Results

A genome-wide association study (GWAS) of 5,966 individuals wasconducted, that identified five IgAN susceptibility loci that influencethe risk of IgA nephropathy. These include 3 distinct intervals in theMHC-II region on chromosome 6p21, with the strongest signal encompassingthe HLA DQB1/DQA1/DRB1 locus (abbreviated as DQB1/DRB1). Imputation ofclassical alleles showed that this signal was partially conveyed by astrong protective effect of the DRB1*1501-DQB1*0602 haplotype. Thesecond signal on Chr. 6p21 encompassed a ˜100 Kb region containing TAP2,TAP1, PSMB8, and PSMB9 genes (TAP2/PSMB9 locus) and the third signal onChr. 6p21 contained the HLA DPA1/DPB1/DPB2 genes (DPA1/DPB2 locus).Three protective alleles were located on Chr. 6p21 (HLA-DQB1/DRB1,PSMB9/TAP1 and DPA1/DPB2 loci), two on Chr. 1q32 (CFHR3/R1 locus) andone on Chr. 22q12 (HORMAD2 locus). Independence of these three regionson Chr. 6p21 was demonstrated by their localization within distinct LDblocks as well as genome-wide significant associations after rigorousconditional analyses. EXAMPLE 1. There was a significant associationwithin the Complement factor H (CFH) gene cluster on Chr. 1q32, wherealleles tagging a common deletion in the CFHR3 and CFHR1 genes imparteda significant protective effect (CFHR3/R1 locus). These five lociindividually conferred a moderate risk of disease (OR 1.25-1.59), buttogether explained 4-5% of the variation in risk across the populationsexamined.

The GWAS study identified five minor alleles at these five loci thatconfer independent protection against the risk of developing IgAnephropathy (Table 2). These five protective alleles were identified bythe presence of five respective independent SNPs: rs6677604 (A),rs9275596 (C), rs9357155 (A), rs1883414 (T), and rs2412971(A). In orderto show that this result was not artifact, five redundant SNPs wereidentified corresponding to the five alleles that also had anindependent protective effect on the risk of developing IgAN; these arers3766404 (C), rs2856717 (T), rs2071543 (A), rs3129269 (T), rs2412973(A). If a subject's DNA has any of the ten SNPs identified above, thesubject has at least one prot allele that reduces the risk of developingIgAN.

A subject with no protective alleles is not necessarily at a higher thannormal risk for developing IgAN, however, the presence of one or more ofthese protective alleles have a cumulative effect to reduce the risk ofa subject developing IgAN. (Table 4.)

To follow-up the GWAS studies and better assess the risk imparted bysusceptibility alleles in diverse populations, a replication study wasconducted in eight independent case-control cohorts of Asian, Europeanand African-American ancestry (N=4,789), followed by meta-analysis withrisk-score modeling in 12 cohorts (N=10,755), and geospatial analysis in85 world populations. Four susceptibility loci were robustly replicatedand all five loci showed genome-wide significance in the combined cohort(P=5×10⁻³²-3×10⁻¹⁰), with heterogeneity detected only at the PSMB9/TAP1locus (I²=0.60). Two new independent risk alleles were identified withinthe HLA-DQB1/DRB1 locus, rs9275224 (A) and rs2856717 (T), definingmultiple risk and protective haplotypes within this interval. A newgenetic interaction between loci on Chr.1p36 and Chr.22q22 was alsodiscovered. Example 2.

Between the two studies a total of seven loci harboring sevenindependent protective alleles and 12 corresponding SNPs wereidentified. These seven independent protective alleles can be identifiedby the presence of seven respective independent SNPs: (i) rs6677604 (A),rs9275596 (C), rs9357155 (A), rs1883414 (T), rs2412971(A), rs9275224 (A)and rs2856717 (T); or (ii) rs3766404 (C), rs2856717 (T), rs2071543 (A),rs3129269 (T), rs2412973 (A), rs9275224 (A) and rs2856717 (T).[rs6677604 (A), rs3766404 (C)], [rs9357155 (A), rs2071543 (A)],[rs1883414 (T), rs3129269 (T)] and [rs2412971(A), rs2412973 (A)].

In the embodiments of the invention it can be determined whether asubject has a protective allele for IgAN by analyzing the subject's DNAfor any or preferably all of the seven IgAN protective alleles.

A stepwise regression algorithm in the entire cohort defined a new riskscore that retained the 7 SNPs exhibiting an independent protectiveeffect on IgAN: rs6677604 (A), rs9275224 (A), rs2856717 (T), rs9275596(C), rs9357155 (A), rs1883414 (T) and rs2412971 (A). Some embodimentsare directed to computing a subject's genetic risk score based on theweighted sum of the number of protected alleles at each locus,multiplied by the log of the odds ratio for each of the individual loci.A genetic risk score can also be based on the redundant SNPs identifiedin the first GWAS study for five of the seven protective alleles,rs3766404 (C), rs2856717 (T), rs2071543 (A), rs3129269 (T), rs2412973(A), and the two alleles identified in the second larger study:rs9275224 (A) and rs2856717 (T).

Another set of embodiments are directed to microarrays of boundoligonucleotide probes that are complementary to and specificallyhybridize to any or all of the twelve SNPs to screen a patient's DNA forone or more IgAN protective alleles. In an embodiment the array isdesigned to detect seven SNPS that represent seven independentprotective alleles. In this list the SNPs in brackets are redundant. Ascreen can include either SNP to indicate the protective allele:[rs6677604 (A), rs3766404 (C)], rs9275596 (C), rs9275224, rs2856717[rs9357155 (A), rs2071543 (A)], [rs1883414 (T), rs3129269 (T)] and[rs2412971(A), rs2412973 (A)].

A set of embodiments are directed to a method for determining if asubject has a reduced risk of the developing IgAN due to the presence ofone or more protective alleles.

Knowing the genetic risk for developing IgAN is also important, forexample in determining whether to begin treatment of a subject that hassymptoms of the disease such as blood or protein or both in the urineand/or reduced kidney function. If the subject has a reduced geneticrisk of developing IgAN because one or more protective alleles aredetected in a DNA sample, then it is not necessary to begin drugtherapy. However, if the subject has an increased genetic risk ofdeveloping IgAN due to the absence of at least one protective allele,then drug therapy should be initiated. The typical therapy for treatingIgAN with therapeutically effective amounts of one or more steroids.

A set of embodiments is directed to methods for determining a geneticrisk score for a subject by calculating the weighted sum of the numberof protected alleles at each locus, multiplied by the log of the oddsratio for each of the individual loci. If a subject is prone to blood orprotein in the urine or reduced kidney function, a physician may want totest the patient for the risk of developing IgAN.

Certain additional embodiments of the invention are based in part on thediscovery that certain protective alleles (rs6677604 and rs3766404) arelocated in a 100-kb segment on Chr. 1q31-q32.1 in intron 12 of CFH thatcontains complement factor H (CFH) and the related CFHR3, CFHR1, CFHR4,CHFR2, CFHR5 genes. It was discovered that this segment encompasses anallele that perfectly tags a common deletion spanning the entire CFHR1and CFHR3 genes (CFHR1,3Δ)^(22,23). This protective allele confers atwo-fold protection in the development of IgA nephropathy in Asian andCaucasian population (p=1×10-9). It has been discovered that inhibitingthe expression of the proteins CFHR1 or CFHR3 is the basis of a newtherapy for treating or preventing IgAN, since none of the subjects withthese deletions had any detectable adverse side effects.

Another set of embodiments are directed to methods for treating orpreventing IgAN by reducing the expression or biological activity ofCFHR1 or CFHR3 or both in a subject by administering therapeuticallyeffective amounts of inhibitory oligonucleotides, either systemically orpossibly locally to the kidney, or administrating an antibody or a smallchemical inhibitor that inhibit the proteins.

The gene, cDNA and mRNA sequences for CHFR1 and CHFR3 are known andavailable publicly. It is routine to design inhibitory oligonucleotidesbased on this sequence information. FHR-1 gene is also known as CHFR1,CFHL1, CFHL, FHR1 and HFL1. The reference form of human HFR-1 cDNA (seeEstaller et al., 1991, J. Immunol. 146:3190-3196) and genomic sequenceshave been determined. encodes a polypeptide 330 amino acids in lengthhaving a predicted molecular weight of 39 kDa. cDNA and amino acidsequence data for human FHR-1 are found in the EMBL/GenBank DataLibraries under accession number M65292. The FHR-1 gene sequence isfound under GenBank accession number AL049741. Homo sapiens complementfactor H-related 1 (CFHR1), mRNA is publically available as RefSeqSummary (NM_002113): This gene encodes a secreted protein belonging tothe complement factor H protein family. Genomic Size: 12459.

The FHR-3 gene is also known as CFHR3, CFHL3, FHR3 and HLF4. Thereference form of human HFR-3 cDNA (see Strausberg et al., Proc. Natl.Acad. Sci USA 99:16899-16903) and genomic sequences have beendetermined. The FHR-3 cDNA encodes a polypeptide 330 amino acids inlength having a predicted molecular weight of 38 kDa. cDNA and aminoacid sequence data for human FHR-3 are found in the EMBL/GenBank DataLibraries under accession number BC058009. The FHR-3 gene sequence isfound under GenBank accession number AL049741. CHFR3 Accession Number,NM_021023.

Other protective alleles have been discovered that provide a two-foldreduction in the risk of developing IgAN. One locus is tagged by SNPrs9357155 that lies in a ˜100 kb segment of LD and lies 128 kbcentromeric to rs9275596 in the second independent interval at 6p21 andcontains TAP2, TAP1, PSMB8, and PSMB9, interferon-regulated genes thathave been implicated in antigen generation and processing forpresentation by MHC I molecules; they also play an important role inmodulation of cytokine production and cytotoxic T-cell response. PSMB8expression is increased in PBMCs from IgAN subjects.³⁵. To ourknowledge, this locus has not been identified in any prior GWAS. It hasbeen discovered that the supporting SNP rs2071543 (also on chromosome 6)is a missense variant in PSMB8 (Q49K) that is at a position which iscompletely conserved among all orthologs.

As described above, the methods are also useful in genetic confirmationsof a diagnosis of IgAN, or to determine a therapeutic regimen for asubject.

Certain other embodiments, described in detail below, are directed tomethods for identifying compounds such as small molecules, peptides,antibodies or fragments thereof that bind to a target protein CFHR1 orCFHR 3, or a biologically active fragment or variant thereof, therebyreducing the biological activity of the protein. Such inhibitorycompounds have potential therapeutic utility in treating IgAN in asubject.

Definitions

Unless otherwise noted, technical terms are used according toconventional usage. Definitions of common terms in molecular biology maybe found in Benjamin Lewin, Genes V, published by Oxford UniversityPress, 1994 (ISBN 0-19-854287-9); Kendrew et al. (eds.), TheEncyclopedia of Molecular Biology, published by Blackwell Science Ltd.,1994 (ISBN 0-632-02182-9); and Robert A. Meyers (ed.), Molecular Biologyand Biotechnology: a Comprehensive Desk Reference, published by VCHPublishers, Inc., 1995 (ISBN 1-56081-569-8).

“Allele”: A particular form of a genetic locus, distinguished from otherforms by its particular nucleotide sequence, or one of the alternativepolymorphisms found at a polymorphic site.

“Correlation”: A correlation between a phenotypic trait and the presenceor absence of a genetic marker (or haplotype or genotype) can beobserved by measuring the phenotypic trait and comparing it to datashowing the presence or absence of one or more genetic markers. Somecorrelations are stronger than others, meaning that in some instancessubjects will display a particular genetic marker (i.e., 100%correlation. In the present application, a haplotype which containsinformation relating to the presence or absence of multiple markers iscorrelated to a genetic predisposition to develop IgAN.

“Genetic predisposition”: Susceptibility of a subject to a disease, suchas IgAN. Detecting a genetic predisposition includes detecting the riskof developing the disease, and determining the susceptibility of thatsubject to developing the disease or to having a poor prognosis for thedisease. Thus, if a subject has a genetic predisposition to a diseasethey do not necessarily develop the disease but are at a higher thannormal risk for developing the disease.

“Gene”: A segment of DNA that contains the coding sequence for aprotein, wherein the segment may include promoters, exons, introns, andother untranslated regions that control expression.

“Genotype”: A genotype is the genetic makeup of a cell, an organism, oran individual (i.e. the specific allele makeup of the individual)usually with reference to a specific character under consideration.

“Protective allele” means an allele that confers a lower risk ofdeveloping IgA nephropathy.

“Haplotype”: is a combination of alleles (DNA sequences) at adjacentlocations (loci) on the chromosome that are transmitted together. Ahaplotype may be one locus, several loci, or an entire chromosomedepending on the number of recombination events that have occurredbetween a given set of loci.

“Linkage”: The association of two or more loci at positions on the samechromosome, such that recombination between the two loci is reduced to aproportion significantly less than 50%. The term linkage can also beused in reference to the association between one or more loci and atrait if an allele (or alleles) and the trait, or absence thereof, areobserved together in significantly greater than 50% of occurrences. Alinkage group is a set of loci, in which all members are linked eitherdirectly or indirectly to all other members of the set.

“Linkage Disequilibrium (LD)”: Co-occurrence of two genetic loci (e.g.,markers) at a frequency greater than expected for independent loci basedon the allele frequencies. Linkage disequilibrium (LD) typically occurswhen two loci are located close together on the same chromosome. Whenalleles of two genetic loci (such as a marker locus and a causal locus)are in strong LD, the allele observed at one locus (such as a markerlocus) is predictive of the allele found at the other locus (forexample, a causal locus contributing to a phenotypic trait).

“Locus”: A location on a chromosome or DNA molecule corresponding to agene or a physical or phenotypic feature, where physical featuresinclude polymorphic sites.

“Mutation”: Any change of a nucleic acid sequence as a source of geneticvariation.

“Odds Ratio”: A calculation performed by analysis of a two by twocontingency table. In one example, the first column provides a riskindicator in the absence of a disease (e.g., IgAN). The second columnprovides the same risk indicator in the presence of the same disease.The first row lists the risk indicator in the absence of a risk factor(such as race) and the second row lists the same risk indicator in thepresence of the same risk factor (i.e., race). The Odds Ratio (OR) isdetermined as the product of the two diagonal entries in the contingencytable divided by the product of the two off-diagonal entries of thecontingency table. An OR of 1 is indicative of no association.Accordingly, very large or very small ORs are indicative of a strongassociation between the factors under investigation. The OR isindependent of the ratio of cases or controls in a study, group orsubset.

“Polymorphism”: A variation in a gene sequence. Polymorphisms can bereferred to, for instance, by the nucleotide position at which thevariation exists, by the change in amino acid sequence caused by thenucleotide variation, or by a change in some other characteristic of thenucleic acid molecule or protein that is linked to the variation. In theinstant application “polymorphism” refers a traditional definitionmeaning that the minor allele frequency must be greater than at least1%.

A “single nucleotide polymorphism (SNP)” is a single base (nucleotide)polymorphism in a DNA sequence among individuals in a population. SNPgenotyping is the measurement of genetic variations of single nucleotidepolymorphisms (SNPs) between members of a species. SNPs are one of themost common types of genetic variation. An SNP is a single base pairmutation at a specific locus, usually consisting of two alleles (wherethe rare allele frequency is >1%). SNPs are involved in the etiology ofmany human diseases.

A “tag SNP” is a SNP that by itself or in combination with additionalTag SNPs indicates the presence of a specific haplotype, or of onemember of a group of haplotypes. The haplotype or haplotypes canindicate a genetic factor is associated with risk for disease, thus atag SNP or combination of tag SNPs indicates the presence or absence ofrisk factors for disease. A “tag SNP” is a representative singlenucleotide polymorphism (SNP) in a region of the genome with highlinkage disequilibrium (the non-random association of alleles at two ormore loci) that is associated with a disease, such as IgAN. A tag SNPcan be used to identify other SNPs, such as those with a specified r²value from the tag SNP, which are associated with the disease.

“IgA Nephropathy (IgAN”): A disorder that specifically leads to damageof the kidneys that is normally diagnosed by kidney biopsy showingpredominant deposition Immunoglobulin A on immunofluorescence coupledwith light microscopy showing mesangial proliferation, or expansion IGsinclude IgA nephropathy. IgAN can be chronic or acute. IgA nephropathy(also known as IgA nephritis, IgAN, Berger's disease and synpharyngiticglomerulonephritis) is a form of glomerulonephritis (inflammation of theglomeruli of the kidney). IgA nephropathy is the most commonglomerulonephritis throughout the world. Primary IgA nephropathy ischaracterized by deposition of the IgA antibody in the glomerulus.

“Risk Allele”: A “risk” allele is an allele associated with a particulartype or form of disease. The risk allele identifies a tag singlenucleotide polymorphism that can be used to detect or determine the riskfor a disease, such as IgAN.

“Sample”: means a biological sample obtained from a subject. Forembodiments of the present invention of diagnostic tests forpredisposition to IgA, a blood sample such as whole blood or serum ispreferred. However any biological sample can be used, including, but notlimited to, cells, tissues, and bodily fluids, such as: blood;derivatives and fractions of blood, such as serum; biopsied orsurgically removed tissue, urine; sputum; cerebrospinal fluid or bonemarrow aspirates.

“Subject” shall mean any organism including, without limitation, amammal such as a mouse, a rat, a dog, a guinea pig, a ferret, a rabbitand a primate. In the preferred embodiment, the subject is a humanbeing.

“Therapeutically effective amount”: An amount of a therapeutic agentthat alone, or together with one or more additional therapeutic agents,induces the desired response, such as decreasing the risk of developingIgAN or decreasing the signs and symptoms of IgAN.

“Reference Allele”: A genotype that predominates in a natural populationof organisms that do not have a disease process. The reference genotypediffers from mutant forms.

Unless otherwise explained, all technical and scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which this disclosure belongs.

Nucleic Acids

Methods of isolating and analyzing nucleic acid molecules from abiological sample are routine, for example using PCR to amplify themolecules from the sample, or by using a commercially available kit toisolate DNA. Nucleic acid molecules isolated from a biological samplecan be amplified using routine methods to form nucleic acidamplification products.

Nucleic acid molecules can be prepared for analysis using any techniqueknown to those skilled in the art. Generally, such techniques result inthe production of a nucleic acid molecule sufficiently pure to determinethe presence or absence of one or more variations at one or morelocations in the nucleic acid molecule. Such techniques are describedfor example, in Sambrook, et al., Molecular Cloning: A Laboratory Manual(Cold Spring Harbor Laboratory, New York) (1989), and Ausubel, et al.,Current Protocols in Molecular Biology (John Wiley and Sons, New York)(1997), incorporated herein by reference.

Amplification of nucleic acid molecules: Optionally, the nucleic acidsamples obtained from the subject are amplified prior to detection.Target nucleic acids are amplified to obtain amplification products,including sequences from a tag SNP, can be amplified from the sampleprior to detection. Typically, DNA sequences are amplified, although insome instances RNA sequences can be amplified or converted into cDNA,such as by using RT PCR.

Methods for labeling nucleic acid molecules so they can be detected arewell known. Examples of such labels include non-radiolabels andradiolabels. Non-radiolabels include, but are not limited to an enzyme,chemiluminescent compound, fluorescent compound (such as FITC, Cy3, andCy5), metal complex, hapten, enzyme, colorimetric agent, a dye, orcombinations thereof. Radiolabels include, but are not limited to, ¹²⁵I,³²P and ³⁵S. For example, radioactive and fluorescent labeling methods,as well as other methods known in the art, are suitable for use with thepresent disclosure. In one example, primers used to amplify thesubject's nucleic acids are labeled (such as with biotin, a radiolabel,or a fluorophore). In another example, amplified target nucleic acidsamples are end-labeled to form labeled amplified material. For example,amplified nucleic acid molecules can be labeled by including labelednucleotides in the amplification reactions.

Nucleic acid molecules corresponding to one or more tag SNPs orhaplotype blocks including the tag SNP can also be detected byhybridization procedures using a labeled nucleic acid probe, such as aprobe that detects only one alternative allele at a marker locus. Mostcommonly, the target nucleic acid (or amplified target nucleic acid) isseparated based on size or charge and transferred to a solid support.The solid support (such as membrane made of nylon or nitrocellulose) iscontacted with a labeled nucleic acid probe, which hybridizes to itcomplementary target under suitable hybridization conditions to form ahybridization complex.

Hybridization conditions for a given combination of array and targetmaterial can be optimized using methods known to one of skill in the art(see U.S. Pat. No. 5,981,185). Once the target nucleic acid moleculeshave been hybridized with the labeled probes, the presence of thehybridization complex can be analyzed, for example by detecting thecomplexes. Methods for detecting hybridized nucleic acid complexes arewell known in the art.

“Allele Specific PCR”: Allele-specific PCR differentiates between targetregions differing in the presence of absence of a variation orpolymorphism. PCR amplification primers are chosen based upon theircomplementarity to the target sequence, such as nucleic acid sequence ina haplotype block including a tag SNP, a specified region of an alleleincluding a tag SNP, or to the tag SNP itself. The primers bind only tocertain alleles of the target sequence. This method is described byGibbs, Nucleic Acid Res. 17:12427 2448, 1989, herein incorporated byreference.

“Allele Specific Oligonucleotide Screening Methods”: Further screeningmethods employ the allele-specific oligonucleotide (ASO) screeningmethods (e.g. see Saiki et al., Nature 324:163-166, 1986).Oligonucleotides with one or more base pair mismatches are generated forany particular allele or haplotype block. ASO screening methods detectmismatches between one allele (or haplotype block) in the target genomicor PCR amplified DNA and the other allele (or haplotype block), showingdecreased binding of the oligonucleotide relative to the second allele(i.e. the other allele) oligonucleotide. Oligonucleotide probes can bedesigned that under low stringency will bind to both polymorphic formsof the allele, but which at high stringency, only bind to the allele towhich they correspond. Alternatively, stringency conditions can bedevised in which an essentially binary response is obtained, i.e., anASO corresponding to a variant form of the target gene will hybridize tothat allele (haplotype block), and not to the reference allele(haplotype block).

“Ligase Mediated Allele Detection Method”: Ligase can also be used todetect point mutations, such as the tag SNPs disclosed herein, in aligation amplification reaction (e.g. as described in Wu et al.,Genomics 4:560-569, 1989). The ligation amplification reaction (LAR)utilizes amplification of specific DNA sequence using sequential roundsof template dependent ligation (e.g. as described in Wu, supra, andBarany, Proc. Nat. Acad. Sci. 88:189-193, 1990).

“Denaturing Gradient Gel Electrophoresis”: Amplification productsgenerated using the polymerase chain reaction can be analyzed by the useof denaturing gradient gel electrophoresis. Different alleles (haplotypeblocks) can be identified based on the different sequence-dependentmelting properties and electrophoretic migration of DNA in solution. DNAmolecules melt in segments, termed melting domains, under conditions ofincreased temperature or denaturation. Each melting domain meltscooperatively at a distinct, base-specific melting temperature (T_(M)).Melting domains are at least 20 base pairs in length, and can be up toseveral hundred base pairs in length.

“Non-gel Systems”: Other possible techniques include non-gel systemssuch as TaqMan™ (Perkin Elmer). In this system oligonucleotide PCRprimers are designed that flank the mutation in question and allow PCRamplification of the region. A third oligonucleotide probe is thendesigned to hybridize to the region containing the base subject tochange between different alleles of the gene. This probe is labeled withfluorescent dyes at both the 5′ and 3′ ends. These dyes are chosen suchthat while in this proximity to each other the fluorescence of one ofthem is quenched by the other and cannot be detected. Extension by TaqDNA polymerase from the PCR primer positioned 5′ on the templaterelative to the probe leads to the cleavage of the dye attached to the5′ end of the annealed probe through the 5′ nuclease activity of the TaqDNA polymerase. This removes the quenching effect allowing detection ofthe fluorescence from the dye at the 3′ end of the probe. Thediscrimination between different DNA sequences arises through the factthat if the hybridization of the probe to the template molecule is notcomplete (there is a mismatch of some form) the cleavage of the dye doesnot take place. Thus only if the nucleotide sequence of theoligonucleotide probe is completely complimentary to the templatemolecule to which it is bound will quenching be removed. A reaction mixcan contain two different probe sequences each designed againstdifferent alleles that might be present thus allowing the detection ofboth alleles in one reaction.

“Non-PCR Based Allele detection”: The identification of a DNA sequencecan be made without an amplification step, based on polymorphismsincluding restriction fragment length polymorphisms in a subject and acontrol, such as a family member. Hybridization probes are generallyoligonucleotides which bind through complementary base pairing to all orpart of a target nucleic acid. Probes typically bind target sequenceslacking complete complementarity with the probe sequence depending onthe stringency of the hybridization conditions. The probes can belabeled directly or indirectly, such that by assaying for the presenceor absence of the probe, one can detect the presence or absence of thetarget sequence. Direct labeling methods include radioisotope labeling,such as with ³²P or ³⁵S. Indirect labeling methods include fluorescenttags, biotin complexes which can be bound to avidin or streptavidin, orpeptide or protein tags. Visual detection methods includephotoluminescents, Texas red, rhodamine and its derivatives, red leucodye and 3,3′,5,5′-tetramethylbenzidine (TMB), fluorescein, and itsderivatives, dansyl, umbelliferone and the like or with horse radishperoxidase, alkaline phosphatase and the like.

Nucleic Acid Arrays

Certain embodiments are directed to a microarrays for detecting one ormore protective alleles in a DNA sample, which alleles indicate agenetic predisposition to IgAN in a human subject. The array containsprobes complementary to at least one single nucleotide polymorphismindicating an independent protective allele, preferably probes areincluded for hybridizing all seven independent protective alleles. Eachof the single nucleotide polymorphisms is associated with a specificprotective allele for IgAN and is complementary to the targeted SNP.

It will be readily apparent to one skilled in the art that the exactformulation of probes on an array is not critical as long as the user isable to select probes for inclusion on the array that fulfill thefunction of hybridizing to the targeted SNPs. The array can be modifiedto suit the needs of the user. Thus, analysis of the array can providethe user with information regarding the number and/or presence ofprotective alleles in a given sample. The hybridization of a probecomplementary to a protective allele in an array can indicate that thesubject from whom the sample was derived is at an elevated risk fordeveloping a disease such as IgAN; or alternatively if it hybridizes toa protective allele the subject has a reduced risk.

A wide variety of array formats can be employed in accordance with thepresent disclosure. One example includes a linear array ofoligonucleotide bands, generally referred to in the art as a dipstick.Another suitable format includes a two-dimensional pattern of discretecells (such as 4096 squares in a 64 by 64 array). As is appreciated bythose skilled in the art, other array formats including, but not limitedto slot (rectangular) and circular arrays are equally suitable for use(see U.S. Pat. No. 5,981,185). In one example, the array is formed on apolymer medium, which is a thread, membrane or film. An example of anorganic polymer medium is a polypropylene sheet having a thickness onthe order of about 1 mm (0.001 inch) to about 20 mm although thethickness of the film is not critical and can be varied over a fairlybroad range. Biaxially oriented polypropylene (BOPP) films are alsosuitable in this regard; in addition to their durability, BOPP filmsexhibit a low background fluorescence. In a particular example, thearray is a solid phase, Allele-Specific Oligonucleotides (ASO) basednucleic acid array.

The array formats of the present disclosure can be included in a varietyof different types of formats. A “format” includes any format to whichthe solid support can be affixed, such as microtiter plates, test tubes,inorganic sheets, dipsticks, and the like. For example, when the solidsupport is a polypropylene thread, one or more polypropylene threads canbe affixed to a plastic dipstick-type device; polypropylene membranescan be affixed to glass slides. The particular format is, in and ofitself, unimportant. All that is necessary is that the solid support canbe affixed thereto without affecting the functional behavior of thesolid support or any biopolymer absorbed thereon, and that the format(such as the dipstick or slide) is stable to any materials into whichthe device is introduced (such as clinical samples and hybridizationsolutions).

The arrays of the present disclosure can be prepared by a variety ofapproaches. In one example, oligonucleotide or protein sequences aresynthesized separately and then attached to a solid support (see U.S.Pat. No. 6,013,789). In another example, sequences are synthesizeddirectly onto the support to provide the desired array (see U.S. Pat.No. 5,554,501). Suitable methods for covalently couplingoligonucleotides and proteins to a solid support and for directlysynthesizing the oligonucleotides or proteins onto the support are knownto those working in the field; a summary of suitable methods can befound in Matson et al., Anal. Biochem. 217:306-10, 1994. In one example,the oligonucleotides are synthesized onto the support using conventionalchemical techniques for preparing oligonucleotides on solid supports(see PCT Publication No. WO 85/01051 and PCT Publication No. WO89/10977, or U.S. Pat. No. 5,554,501).

A suitable array can be produced using automated means to synthesizeoligonucleotides in the cells of the array by laying down the precursorsfor the four bases in a predetermined pattern. Briefly, amultiple-channel automated chemical delivery system is employed tocreate oligonucleotide probe populations in parallel rows (correspondingin number to the number of channels in the delivery system) across thesubstrate. Following completion of oligonucleotide synthesis in a firstdirection, the substrate can then be rotated by 90.degree. to permitsynthesis to proceed within a second (2 degrees) set of rows that arenow perpendicular to the first set. This process creates amultiple-channel array whose intersection generates a plurality ofdiscrete cells. In particular examples, the oligonucleotide probes onthe array include one or more labels, which permit detection ofoligonucleotide probe:target sequence hybridization complexes.

Kits

Certain embodiments are directed to kits that can be used to detect SNPsindicating the presence of one or more protective alleles for IgAN in aDNA sample. The disclosed kits include a binding molecule, such as anoligonucleotide probe that selectively hybridizes to an allele of ahaplotype block including a particular known SNP. Alternatively oradditionally, the kits can include one or more isolated primers orprimer pairs for amplifying a target nucleic acid, such as a haplotypeincluding a SNP. For example, the kit can include primers for amplifyinga haplotype including one, two, three, four, five, six, seven, eight,nine, ten SNPs associated with a particular protective allele.

The kit can further include one or more of a buffer solution, aconjugating solution for developing the signal of interest, or adetection reagent for detecting the signal of interest, each in separatepackaging, such as a container. In another example, the kit includes aplurality of size-associated marker target nucleic acid sequences forhybridization with a detection array. The target nucleic acid sequencescan include oligonucleotides such as DNA, RNA, and peptide-nucleic acid,or can include PCR fragments. The kit can also include instructions in atangible form, such as written instructions or in a computer-readableformat.

Inhibitory Oligonucleotides

Other embodiments of the present invention are directed to the use ofinhibitory oligonucleotides such as. antisense DNA or RNA (or chimerasthereof), small interfering RNA (siRNA), micro RNA (miRNA), shorthairpin RNA, ribozymes, supermir, and aptamers, to reduce or inhibitexpression of CFHR1 and CFHR 3, hereafter “the targeted proteins.” ThemRNA and gene sequences encoding the targeted proteins are set forthherein by accession numbers. Based on these known sequences, inhibitoryoligonucleotides that hybridize sufficiently to the respective gene ormRNA encoding the targeted proteins to turn off expression can bereadily designed and engineered using methods known in the art.

Antisense oligonucleotides have been employed as therapeutic moieties inthe treatment of disease states in animals and man. Antisenseoligonucleotide drugs, including ribozymes, have been safely andeffectively administered to humans and numerous clinical trials arepresently underway. It is thus established that oligonucleotides can beuseful therapeutic modalities that can be configured to be useful intreatment regimes for treatment of cells, tissues and animals,especially humans. See for example Agrawal, S. and Zhao, Q. (1998) Curr.Opi. Chemical Biol. Vol. 2, 519-528; Agrawal, S and Zhang, R. (1997)CIBA Found. Symp. Vol. 209, 60-78; and Zhao, Q, et al., (1998),Antisense Nucleic Acid Drug Dev. Vol 8, 451-458; the entire contents ofwhich are hereby incorporated by reference as if fully set forth herein.Anderson, K. O., et al., (1996) Antimicrobial Agents Chemother. Vol. 40,2004-2011, and U.S. Pat. No. 6,828,151 by Borchers, et al.

The oligonucleotides used herein are synthesized in vitro and do notinclude compositions of biological origin. Based on these knownsequences of the targets (genes or mRNA) therapeutic oligonucleotidescan be engineered using methods known in the art. Different combinationsof these therapeutic agents can be formulated for administration to asubject using methods well known in the art.

These nucleic acids act via a variety of mechanisms. siRNA or miRNA candown-regulate intracellular levels of specific proteins through aprocess termed RNA interference (RNAi). Following introduction of siRNAor miRNA into the cell cytoplasm, these double-stranded RNA constructscan bind to a protein termed RISC. RNA-Induced Silencing Complex, orRISC, is a multiprotein complex that incorporates one strand of a smallinterfering RNA (siRNA) or micro RNA (miRNA). RISC uses the siRNA ormiRNA as a template for recognizing complementary mRNA. When it finds acomplementary strand, it activates RNase and cleaves the RNA. Thisprocess is important both in gene regulation by microRNAs and in defenseagainst viral infections, which often use double-stranded RNA as aninfectious vector RNAi can provide down-regulation of specific proteinsby targeting specific destruction of the corresponding mRNA that encodesfor protein synthesis.

The therapeutic applications of RNAi are extremely broad, since siRNAand miRNA constructs can be synthesized with any nucleotide sequencedirected against mRNA encoding a target protein. To date, siRNAconstructs have shown the ability to specifically down-regulate targetproteins in both in vitro and in vivo models and they are currentlybeing evaluated in clinical studies.

Antisense oligonucleotides and ribozymes can also inhibit mRNAtranslation into protein. In the case of antisense constructs, thesesingle stranded deoxynucleic acids have a complementary sequence to thatof the target protein mRNA and can bind to the mRNA by Watson-Crick basepairing. This binding either prevents translation of the target mRNAand/or triggers RNase H degradation of the mRNA transcripts.Consequently, antisense oligonucleotides have tremendous potential forspecificity of action (i.e., down-regulation of a specificdisease-related protein). To date, these compounds have shown promise inseveral in vitro and in vivo models, including models of inflammatorydisease, cancer, and HIV (reviewed in Agrawal, Trends in Biotech.14:376-387 (1996)). Antisense can also affect cellular activity byhybridizing specifically with chromosomal DNA. Advanced human clinicalassessments of several antisense drugs are currently underway.

It is desirable to optimize the stability of the phosphodiesterinternucleotide linkage and minimize its susceptibility to exonucleasesand endonucleases in serum. (Zelphati, O., et al., Antisense. Res. Dev.3:323-338 (1993); and Thierry, A. R., et al., pp 147-161 in GeneRegulation: Biology of Antisense RNA and DNA (Eds. Erickson, R P andIzant, J G; Raven Press, NY (1992)).

Therapeutic nucleic acids being currently being developed do nottypically employ the basic phosphodiester chemistry found in naturalnucleic acids, because of these and other known problems. Modificationshave been made at the internucleotide phosphodiester bridge (e.g., usingphosphorothioate, methylphosphonate or phosphoramidate linkages), at thenucleotide base (e.g., 5-propynyl-pyrimidines), or at the sugar (e.g.,2′-modified sugars) (Uhlmann E., et al. Antisense: ChemicalModifications. Encyclopedia of Cancer, Vol. X., pp 64-81 Academic PressInc. (1997)). Others have attempted to improve stability using 2′-5′sugar linkages (see, e.g., U.S. Pat. No. 5,532,130).

Nucleic acids for use in embodiments of the present invention may be ofvarious lengths, generally dependent upon the particular form of nucleicacid, typically from about 10 to 100 nucleotides in length. In variousrelated embodiments, oligonucleotides, single-stranded, double-stranded,and triple-stranded, may range in length from about 10 to about 50nucleotides, from about 20 o about 50 nucleotides, from about 15 toabout 30 nucleotides, from about 20 to about 30 nucleotides in length.

In particular embodiments, the oligonucleotide (or a strand thereof)specifically hybridizes to or is complementary to a targetpolynucleotide, preferably an mRNA molecule. “Specifically hybridizable”and “complementary” are terms which are used to indicate a sufficientdegree of complementarity such that stable and specific binding occursbetween the DNA or RNA target and the oligonucleotide. It is understoodthat an oligonucleotide need not be 100% complementary to its targetnucleic acid sequence to be specifically hybridizable. Anoligonucleotide is specifically hybridizable when binding of theoligonucleotide to the target interferes with the normal function of thetarget molecule to cause a loss of utility or expression of the target,and there is a sufficient degree of complementarity to avoidnon-specific binding of the oligonucleotide to non-target sequencesunder conditions in which specific binding is desired, i.e., underphysiological conditions in the case of in vivo assays or therapeutictreatment, or, in the case of in vitro assays, under conditions in whichthe assays are conducted. Thus, in other embodiments, thisoligonucleotide includes 1, 2, or 3 base substitutions, e.g. mismatches,as compared to the region of a gene or mRNA sequence that it istargeting or to which it specifically hybridizes.

Small interfering RNA (siRNA) has essentially replaced antisense ODN andribozymes as the next generation of targeted oligonucleotide drugs underdevelopment. SiRNAs are RNA duplexes normally 16-30 nucleotides longthat can associate with a cytoplasmic multi-protein complex known asRNAi-induced silencing complex (RISC). RISC loaded with siRNA mediatesthe degradation of homologous mRNA transcripts; therefore siRNA can bedesigned to knock down protein expression with high specificity. Unlikeother antisense technologies, siRNA function through a natural mechanismevolved to control gene expression through non-coding RNA. This isgenerally considered to be the reason why their activity is more potentin vitro and in vivo than either antisense ODN or ribozymes. A varietyof RNAi reagents, including siRNAs targeting clinically relevanttargets, are currently under pharmaceutical development, as described,e.g., in de Fougerolles, A. et al., Nature Reviews 6:443-453 (2007).

While the first described RNAi molecules were RNA:RNA hybrids comprisingboth an RNA sense and an RNA antisense strand, it has now beendemonstrated that DNA sense:RNA antisense hybrids, RNA sense:DNAantisense hybrids, and DNA:DNA hybrids are capable of mediating RNAi(Lamberton, J. S, and Christian, A. T., (2003) Molecular Biotechnology24:111-119). Thus, the invention includes the use of RNAi moleculescomprising any of these different types of double-stranded molecules. Inaddition, it is understood that RNAi molecules may be used andintroduced to cells in a variety of forms. Accordingly, as used herein,RNAi molecules encompasses any and all molecules capable of inducing anRNAi response in cells, including, but not limited to, double-strandedoligonucleotides comprising two separate strands, i.e. a sense strandand an antisense strand, e.g., small interfering RNA (siRNA);double-stranded oligonucleotide comprising two separate strands that arelinked together by non-nucleotidyl linker; oligonucleotides comprising ahairpin loop of complementary sequences, which forms a double-strandedregion, e.g., shRNAi molecules, and expression vectors that express oneor more polynucleotides capable of forming a double-strandedpolynucleotide alone or in combination with another polynucleotide.

A “single strand siRNA compound” as used herein, is an siRNA compoundwhich is made up of a single molecule. It may include a duplexed region,formed by intra-strand pairing, e.g., it may be, or include, a hairpinor pan-handle structure. Single strand siRNA compounds may be antisensewith regard to the target molecule.

A single strand siRNA compound may be sufficiently long that it canenter the RISC and participate in RISC mediated cleavage of a targetmRNA. A single strand siRNA compound is typically at least 14, and inother embodiments at least 15, 20, 25, 29, 35, 40, or 50 nucleotides inlength. In certain embodiments, it is less than 200, 100, or 60nucleotides in length.

Hairpin siRNA compounds will have a duplex region equal to or at least17, 18, 19, 29, 21, 22, 23, 24, or 25 nucleotide pairs. The duplexregion will may be equal to or less than 200, 100, or 50, in length. Incertain embodiments, ranges for the duplex region are 15-30, 17 to 23,19 to 23, and 19 to 21 nucleotides pairs in length. The hairpin may havea single strand overhang or terminal unpaired region. In certainembodiments, the overhangs are 2-3 nucleotides in length. In someembodiments, the overhang is at the sense side of the hairpin and insome embodiments on the antisense side of the hairpin.

A “double stranded siRNA compound” as used herein, is a siRNA compoundwhich includes more than one, and in some cases two, strands in whichinterchain hybridization can form a region of duplex structure.

The antisense strand of a double stranded siRNA compound may be equal toor at least, 14, 15, 16 17, 18, 19, 25, 29, 40, or 60 nucleotides inlength. It may be equal to or less than 200, 100, or 50, nucleotides inlength. Ranges may be 17 to 25, 19 to 23, and 19 to 21 nucleotides inlength. As used herein, term “antisense strand” means the strand of asiRNA compound that is sufficiently complementary to a target molecule,e.g. a target RNA.

The sense strand of a double stranded siRNA compound may be equal to orat least 14, 15, 16 17, 18, 19, 25, 29, 40, or 60 nucleotides in length.It may be equal to or less than 200, 100, or 50, nucleotides in length.Ranges may be 17 to 25, 19 to 23, and 19 to 21 nucleotides in length.The double strand portion of a double stranded siRNA compound may beequal to or at least, 14, 15, 16 17, 18, 19, 20, 21, 22, 23, 24, 25, 29,40, or 60 nucleotide pairs in length. It may be equal to or less than200, 100, or 50, nucleotides pairs in length. Ranges may be 15-30, 17 to23, 19 to 23, and 19 to 21 nucleotides pairs in length.

In many embodiments, the siRNA compound is sufficiently large that itcan be cleaved by an endogenous molecule, e.g., by Dicer, to producesmaller siRNA compounds, e.g., siRNAs agents

The sense and antisense strands may be chosen such that thedouble-stranded siRNA compound includes a single strand or unpairedregion at one or both ends of the molecule. Thus, a double-strandedsiRNA compound may contain sense and antisense strands, paired tocontain an overhang, e.g., one or two 5′ or 3′ overhangs, or a 3′overhang of 1-3 nucleotides. The overhangs can be the result of onestrand being longer than the other, or the result of two strands of thesame length being staggered. Some embodiments will have at least one 3′overhang. In one embodiment, both ends of a siRNA molecule will have a3′ overhang. In some embodiments, the overhang is 2 nucleotides.

In certain embodiments, the length for the duplexed region is between 15and 30, or 18, 19, 20, 21, 22, and 23 nucleotides in length, e.g., inthe ssiRNA compound range discussed above. ssiRNA compounds can resemblein length and structure the natural Dicer processed products from longdsiRNAs. Embodiments in which the two strands of the ssiRNA compound arelinked, e.g., covalently linked are also included. Hairpin, or othersingle strand structures which provide the required double strandedregion, and a 3′ overhang are also within the invention.

The siRNA compounds described herein, including double-stranded siRNAcompounds and single-stranded siRNA compounds can mediate silencing of atarget RNA, e.g., mRNA, e.g., an mRNA transcript of a gene that encodesa protein. A gene may also be targeted. In general, the RNA to besilenced is an endogenous gene or a pathogen gene. In addition, RNAsother than mRNA, e.g., tRNAs, and viral RNAs, can also be targeted.

As used herein, the phrase “mediates RNAi” refers to the ability tosilence, in a sequence specific manner, a target RNA. While not wishingto be bound by theory, it is believed that silencing uses the RNAimachinery or process and a guide RNA, e.g., an ssiRNA compound of 21 to23 nucleotides.

MicroRNAs

Micro RNAs (miRNAs) are a highly conserved class of small RNA moleculesthat are transcribed from DNA in the genomes of plants and animals, butare not translated into protein. Processed miRNAs are single stranded(about 17-25 nucleotide (nt)) RNA molecules that become incorporatedinto the RNA-induced silencing complex (RISC) and have been identifiedas key regulators of development, cell proliferation, apoptosis anddifferentiation. They are believed to play a role in regulation of geneexpression by binding to the 3′-untranslated region of specific mRNAs.RISC mediates down-regulation of gene expression through translationalinhibition, transcript cleavage, or both. RISC is also implicated intranscriptional silencing in the nucleus of a wide range of eukaryotes.

Antisense Oligonucleotides: In one embodiment, a nucleic acid is anantisense oligonucleotide directed to a target polynucleotide. The term“antisense oligonucleotide” or simply “antisense” is meant to includeoligonucleotides that are complementary to a targeted polynucleotidesequence. Antisense oligonucleotides are single strands of DNA or RNAthat are complementary to a chosen sequence, e.g. a target gene mRNA.Antisense oligonucleotides are thought to inhibit gene expression bybinding to a complementary mRNA. Binding to the target mRNA can lead toinhibition of gene expression either by preventing translation ofcomplementary mRNA strands by binding to it or by leading to degradationof the target mRNA Antisense DNA can be used to target a specific,complementary (coding or non-coding) RNA. If binding takes places thisDNA/RNA hybrid can be degraded by the enzyme RNase H. In particularembodiment, antisense oligonucleotides contain from about 10 to about 50nucleotides, more preferably about 15 to about 30 nucleotides. The termalso encompasses antisense oligonucleotides that may not be exactlycomplementary to the desired target gene. Thus, the invention can beutilized in instances where non-target specific-activities are foundwith antisense, or where an antisense sequence containing one or moremismatches with the target sequence is the most preferred for aparticular use.

Antisense oligonucleotides have been demonstrated to be effective andtargeted inhibitors of protein synthesis, and, consequently, can be usedto specifically inhibit protein synthesis by a targeted gene. Theefficacy of antisense oligonucleotides for inhibiting protein synthesisis well established. See for example (U.S. Pat. No. 5,739,119 and U.S.Pat. No. 5,759,829); (Jaskulski et al., Science. 1988 Jun. 10;240(4858):1544-6; Vasanthakumar and Ahmed, Cancer Commun. 1989;1(4):225-32; Penis et al., Brain Res Mol Brain Res. 1998 Jun. 15;57(2):310-20; U.S. Pat. No. 5,801,154; U.S. Pat. No. 5,789,573; U.S.Pat. No. 5,718,709 and U.S. Pat. No. 5,610,288); (U.S. Pat. No.5,747,470; U.S. Pat. No. 5,591,317 and U.S. Pat. No. 5,783,683).

Methods of producing antisense oligonucleotides are known in the art andcan be readily adapted to produce an antisense oligonucleotide thattargets any polynucleotide sequence. Selection of antisenseoligonucleotide sequences specific for a given target sequence is basedupon analysis of the chosen target sequence and determination ofsecondary structure, binding energy, and relative stability. Antisenseoligonucleotides may be selected based upon their relative inability toform dimers, hairpins, or other secondary structures that would reduceor prohibit specific binding to the target mRNA in a host cell. Highlypreferred target regions of the mRNA include those regions at or nearthe AUG translation initiation codon and those sequences that aresubstantially complementary to 5′ regions of the mRNA. These secondarystructure analyses and target site selection considerations can beperformed, for example, using v.4 of the OLIGO primer analysis software(Molecular Biology Insights) and/or the BLASTN 2.0.5 algorithm software(Altschul et al., Nucleic Acids Res. 1997, 25(17):3389-402).

Aptamers: Aptamers are nucleic acid or peptide molecules that bind to aparticular molecule of interest with high affinity and specificity(Tuerk and Gold, Science 249:505 (1990); Ellington and Szostak, Nature346:818 (1990)). DNA or RNA aptamers have been successfully producedwhich bind many different entities from large proteins to small organicmolecules. See Eaton, Curr. Opin. Chem. Biol. 1:10-16 (1997), Famulok,Curr. Opin. Struct. Biol. 9:324-9 (1999), and Hermann and Patel, Science287:820-5 (2000). Aptamers may be RNA or DNA based, and may include ariboswitch. Regulatory elements are known as riboswitches and aredefined as mRNA elements that bind metabolites or metal ions as ligandsand regulate mRNA expression by forming alternative structures inresponse to this ligand binding (FIG. 1; Nudler & Mironov 2004; Tucker &Breaker 2005; Winkler 2005). Although they can bind proteins likeantibodies, aptamers are not immunogenic, even at doses up to 1000 timesthe therapeutic dose in primates.

A riboswitch is a part of an mRNA molecule that can directly bind asmall target molecule, and whose binding of the target enables it toregulate its own activity, depending on the presence or absence of itstarget molecule. Riboswitches are most often located in the 5′untranslated region (5′ UTR; a stretch of RNA that precedes thetranslation start site) of bacterial mRNA. There they regulate theocclusion of signals for transcription attenuation or translationinitiation. Edwards, A. L. et al., (2010) Riboswitches: A Common RNARegulatory Element. Nature Education 3(9):9.

Generally, aptamers are engineered through repeated rounds of in vitroselection or equivalently, SELEX (systematic evolution of ligands byexponential enrichment) to bind to various molecular targets such assmall molecules, proteins, nucleic acids, and even cells, tissues andorganisms. The aptamer may be prepared by any known method, includingsynthetic, recombinant, and purification methods, and may be used aloneor in combination with other aptamers specific for the same target.Further, as described more fully herein, the term “aptamer” specificallyincludes “secondary aptamers” containing a consensus sequence derivedfrom comparing two or more known aptamers to a given target.

Ribozymes: According to another embodiment of the invention, targetedmRNA is inhibited by ribozymes, which have specific catalytic domainsthat possess endonuclease activity (Kim and Cech, Proc Natl Acad SciUSA. 1987 December; 84(24):8788-92; Forster and Symons, Cell. 1987 Apr.24; 49(2):211-20). For example, a large number of ribozymes acceleratephosphoester transfer reactions with a high degree of specificity, oftencleaving only one of several phosphoesters in an oligonucleotidesubstrate (Cech et al., Cell. 1981 December; 27(3 Pt 2):487-96; Micheland Westhof, J Mol. Biol. 1990 Dec. 5; 216(3):585-610; Reinhold-Hurekand Shub, Nature. 1992 May 14; 357(6374):173-6). This specificity hasbeen attributed to the requirement that the substrate bind via specificbase-pairing interactions to the internal guide sequence (“IGS”) of theribozyme prior to chemical reaction.

At least six basic varieties of naturally-occurring enzymatic RNAs areknown presently. Each can catalyze the hydrolysis of RNA phosphodiesterbonds in trans (and thus can cleave other RNA molecules) underphysiological conditions. In general, enzymatic nucleic acids act byfirst binding to a target RNA. Such binding occurs through the targetbinding portion of a enzymatic nucleic acid which is held in closeproximity to an enzymatic portion of the molecule that acts to cleavethe target RNA. Thus, the enzymatic nucleic acid first recognizes andthen binds a target RNA through complementary base-pairing, and oncebound to the correct site, acts enzymatically to cut the target RNA.Strategic cleavage of such a target RNA will destroy its ability todirect synthesis of an encoded protein. After an enzymatic nucleic acidhas bound and cleaved its RNA target, it is released from that RNA tosearch for another target and can repeatedly bind and cleave newtargets.

The enzymatic nucleic acid molecule may be formed in a hammerhead,hairpin, a hepatitis delta virus, group I intron or RNaseP RNA (inassociation with an RNA guide sequence) or Neurospora VS RNA motif, forexample. Specific examples of hammerhead motifs are described by Rossiet al. Nucleic Acids Res. 1992 Sep. 11; 20(17):4559-65. Examples ofhairpin motifs are described by Hampel et al. (Eur. Pat. Appl. Publ. No.EP 0360257), Hampel and Tritz, Biochemistry 1989 Jun. 13;28(12):4929-33; Hampel et al., Nucleic Acids Res. 1990 Jan. 25;18(2):299-304 and U.S. Pat. No. 5,631,359. An example of the hepatitisvirus motif is described by Perrotta and Been, Biochemistry. 1992 Dec.1; 31(47):11843-52; an example of the RNaseP motif is described byGuerrier-Takada et al., Cell. 1983 December; 35(3 Pt 2):849-57;Neurospora VS RNA ribozyme motif is described by Collins (Saville andCollins, Cell. 1990 May 18; 61(4):685-96; Saville and Collins, Proc NatlAcad Sci USA. 1991 Oct. 1; 88(19):8826-30; Collins and Olive,Biochemistry. 1993 Mar. 23; 32(11):2795-9); and an example of the GroupI intron is described in U.S. Pat. No. 4,987,071. Ribozyme constructsneed not be limited to specific motifs mentioned herein. Methods ofproducing a ribozyme targeted to any polynucleotide sequence are knownin the art. Ribozymes may be designed as described in Int. Pat. Appl.Publ. No. WO 93/23569 and Int. Pat. Appl. Publ. No. WO 94/02595, andsynthesized to be tested in vitro and in vivo, as described therein.Ribozyme activity can be optimized by altering the length of theribozyme binding arms or chemically synthesizing ribozymes withmodifications that prevent their degradation by serum ribonucleases (seee.g., Int. Pat. Appl. Publ. No. WO 92/07065; Int. Pat. Appl. Publ. No.WO 93/15187; Int. Pat. Appl. Publ. No. WO 91/03162; Eur. Pat. Appl.Publ. No. 92110298.4; U.S. Pat. No. 5,334,711; and Int. Pat. Appl. Publ.No. WO 94/13688, which describe various chemical modifications that canbe made to the sugar moieties of enzymatic RNA molecules), modificationswhich enhance their efficacy in cells, and removal of stem II bases toshorten RNA synthesis times and reduce chemical requirements.

Supermir: A supermir refers to a single stranded, double stranded orpartially double stranded oligomer or polymer of ribonucleic acid (RNA)or deoxyribonucleic acid (DNA) or both or modifications thereof, whichhas a nucleotide sequence that is substantially identical to an miRNAand that is antisense with respect to its target. This term includesoligonucleotides composed of naturally-occurring nucleobases, sugars andcovalent internucleoside (backbone) linkages and which contain at leastone non-naturally-occurring portion which functions similarly. Suchmodified or substituted oligonucleotides are preferred over native formsbecause of desirable properties such as, for example, enhanced cellularuptake, enhanced affinity for nucleic acid target and increasedstability in the presence of nucleases. In a preferred embodiment, thesupermir does not include a sense strand, and in another preferredembodiment, the supermir does not self-hybridize to a significantextent. A supermir can have secondary structure, but it is substantiallysingle-stranded under physiological conditions. A supermir that issubstantially single-stranded is single-stranded to the extent that lessthan about 50% (e.g., less than about 40%, 30%, 20%, 10%, or 5%) of thesupermir is duplexed with itself. The supermir can include a hairpinsegment, e.g., sequence, preferably at the 3′ end can self hybridize andform a duplex region, e.g., a duplex region of at least 1, 2, 3, or 4and preferably less than 8, 7, 6, or n nucleotides, e.g., 5 nucleotides.The duplexed region can be connected by a linker, e.g., a nucleotidelinker, e.g., 3, 4, 5, or 6 dTs, e.g., modified dTs. The supermir isduplexed with a shorter oligo, e.g., of 5, 6, 7, 8, 9, or 10 nucleotidesin length, e.g., at one or both of the 3′ and 5′ end or at one end andin the non-terminal or middle of the supermir.

Oligonucleotide Modifications: Unmodified oligonucleotides may be lessthan optimal in some applications, e.g., unmodified oligonucleotides canbe prone to degradation by e.g., cellular nucleases. Nucleases canhydrolyze nucleic acid phosphodiester bonds. However, chemicalmodifications of oligonucleotides can confer improved properties, and,e.g., can render oligonucleotides more stable to nucleases.

As oligonucleotides are polymers of subunits or monomers, many of themodifications described below occur at a position which is repeatedwithin an oligonucleotide, e.g., a modification of a base, a sugar, aphosphate moiety, or the non-bridging oxygen of a phosphate moiety. Itis not necessary for all positions in a given oligonucleotide to beuniformly modified, and in fact more than one of the aforementionedmodifications may be incorporated in a single oligonucleotide or even ata single nucleoside within an oligonucleotide.

In some cases the modification will occur at all of the subjectpositions in the oligonucleotide but in many, and in fact in most casesit will not. By way of example, a modification may only occur at a 3′ or5′ terminal position, may only occur in the internal region, may onlyoccur in a terminal region, e.g. at a position on a terminal nucleotideor in the last 2, 3, 4, 5, or 10 nucleotides of an oligonucleotide. Amodification may occur in a double strand region, a single strandregion, or in both. A modification may occur only in the double strandregion of a double-stranded oligonucleotide or may only occur in asingle strand region of a double-stranded oligonucleotide. E.g., aphosphorothioate modification at a non-bridging oxygen position may onlyoccur at one or both termini, may only occur in a terminal region, e.g.,at a position on a terminal nucleotide or in the last 2, 3, 4, 5, or 10nucleotides of a strand, or may occur in double strand and single strandregions, particularly at termini. The 5′ end or ends can bephosphorylated.

A modification described herein may be the sole modification, or thesole type of modification included on multiple nucleotides, or amodification can be combined with one or more other modificationsdescribed herein. The modifications described herein can also becombined onto an oligonucleotide, e.g., different nucleotides of anoligonucleotide have different modifications described herein.

In some embodiments it is particularly preferred, e.g., to enhancestability, to include particular nucleobases in overhangs, or to includemodified nucleotides or nucleotide surrogates, in single strandoverhangs, e.g., in a 5′ or 3′ overhang, or in both. E.g., it can bedesirable to include purine nucleotides in overhangs. In someembodiments all or some of the bases in a 3′ or 5′ overhang will bemodified, e.g., with a modification described herein. Modifications caninclude, e.g., the use of modifications at the 2′ OH group of the ribosesugar, e.g., the use of deoxyribonucleotides, e.g., deoxythymidine,instead of ribonucleotides, and modifications in the phosphate group,e.g., phosphothioate modifications. Overhangs need not be homologouswith the target sequence.

Modifications to oligonucleotides that come within the scope of theinvention include modifications to the Phosphate Group for example toincrease resistance of the oligoribonucleotide to nucleolytic breakdown,or Replacement of the Phosphate Group by non-phosphorus containingconnectors, or Replacement of Ribophosphate Backbone wherein thephosphate linker and ribose sugar are replaced by nuclease resistantnucleoside or nucleotide surrogates. or Sugar Modifications of all orsome of the sugar groups of the ribonucleic acid. E.g., the 2′ hydroxylgroup (OH) can be modified or replaced with a number of different “oxy”or “deoxy” substituents to enhance stability.

Screening for Compounds Bind to and Inhibit CFHR1 and CFHR 3

A set of embodiments are directed to methods for identifying compounds(herein also ligands) such as small molecules, peptides, antibodies orfragments thereof that bind to a target protein that is either CFHR1 orCFHR 3, or a biologically active fragment or variant thereof, therebyreducing the biological activity of the protein. Such inhibitorycompounds have potential therapeutic utility in treating IgAN in asubject.

In some embodiments the compound is selected from a library of compoundsincluding small molecules or peptides, carbohydrates, organic moleculesor antibodies. The compound can be selected from a combinatorial libraryof compounds.

In some embodiments the compounds are optionally bound to a solidsupport, preferably an array wherein the location and identity of thecompound are known, and the target protein is labeled for easydetection. The binding of the compound to the target can be eithernon-covalent interaction or covalent. Non-covalent binding refers to anassociation that may be disrupted by methods well known to those skilledin the art, such as the addition of an appropriate solvent, or a changein ionic conditions. Conversely, when a covalent linkage is formed theprotein will not be released from the compound by ionic conditions andsolvents that would disrupt non-covalent binding.

A “compound” as defined herein is an entity which has an intrinsicbinding affinity for the target (CHFR1 or CHFR3). The compound can be amolecule, or a portion of a molecule which binds the target. Thecompounds are typically small organic molecules, but may also be othersequence-specific binding molecules, such as peptides (D-, L- or amixture of D- and L-), peptidomimetics, complex carbohydrates or otheroligomers of individual units or monomers which bind specifically to thetarget. The term also includes various derivatives and modificationsthat are introduced in order to enhance binding to the target. Compoundsthat inhibit a biological activity of a target molecule are called“inhibitors” of the target

“Small molecules” are usually less than about 10 kDa molecular weight,and include but are not limited to synthetic organic or inorganiccompounds, peptides, (poly)nucleotides, (oligo)saccharides and the like.Small molecules specifically include small non-polymeric (i.e. notpeptide or polypeptide) organic and inorganic molecules. Manypharmaceutical companies have extensive libraries of such molecules,which can be conveniently screened by using the against the targetproteins. Preferred small molecules have molecular weights of less thanabout 1000 Da, more preferably about 500 Da, and most preferably about250 Da.

The phrase “adjusting the conditions” as used herein refers tosubjecting a target protein, to any individual, combination or series ofreaction conditions or reagents necessary to cause a covalent bond toform between the compound and the target.

“Functional variants” of a molecule herein are variants having anactivity in common with the reference molecule

“Active” or “activity” means a qualitative biological property of thetargeted protein. Biological property is not limiting.

Direct, non-competitive binding assays are advantageously used to screenlibraries of compounds for those that selectively bind to a preselectedtarget. Binding is detected using any physical method that measures thealtered physical property of the compound bound to the target protein.The structure of the bound compound can also be determined. The methodsused will depend, in part, on the nature of the library screened. Themethods of the present invention provide a simple, sensitive assay forhigh-throughput screening of libraries of compounds to identifycompounds that inhibit or reduce the biological activity of the targetprotein.

As used herein, a “library” refers to a plurality of compounds withwhich a target protein molecule is contacted. A library can be acombinatorial library, e.g., a collection of compounds synthesized usingcombinatorial chemistry techniques, or a collection of unique chemicalsof low molecular weight (less than 1000 daltons) that each occupy aunique three-dimensional space.

As used herein, a “label” or “detectable label” is a composition that isdetectable, either directly or indirectly, by spectroscopic,photochemical, biochemical, immunochemical, or chemical means.

Methods are described in which a preselected target protein having adetectable label is used to screen a library of compounds. The methodscan also be adapted to put the label on the compound. Any complexesformed between the target and a member of the library are identifiedusing methods that detect the labeled target bound to a compound/testcompound. In an embodiment compounds are bound to a solid support suchas a bead. Binding can also be assayed in solution where the compoundsare not bound to a support. Bound complexes can be separated from theunbound target in the liquid phase by a known means such as, but notlimited to, flow cytometry, affinity chromatography, manual batch modeseparation, suspension of beads in electric fields, and microwave. Thedetectably labeled complex can then be identified by the label on thetarget protein and removed from the uncomplexed, unlabeled compounds inthe library.

Where a solid support is used, the method for identifying a testcompound that binds to a target includes (a) contacting a detectablylabeled target molecule with a library of solid support-attachedcompounds under conditions that permit direct binding of the labeledtarget to a member of the library of solid support-attached compounds sothat a detectably labeled target target:support-attached test compoundcomplex is formed; (b) separating the detectably labeledtarget:support-attached test compound complex formed in step (a) fromuncomplexed target molecules and compounds; and optionally (c)determining a structure of the test compound of the RNA support-attachedtest compound complex.

The compound that binds to the target can then be tested in a biologicalassay (in vitro or cell based or chemical) to determine if it reducesthe biological activity of the target.

Detectable labels include a fluorescent dye, phosphorescent dye,ultraviolet dye, infrared dye, visible radiolabel, enzyme, spectroscopiccolorimetric label, affinity tag, or nanoparticle.

Libraries of Small Molecules

Libraries screened using the methods of the present invention cancomprise a variety of types of compounds. In all of the embodimentsdescribed below, all of the libraries can be optionally synthesized onsolid supports or the compounds of the library can be attached to solidsupports by linkers.

In some embodiments, the compounds are peptide molecules. In anon-limiting example, peptide molecules can exist in a phage displaylibrary. In other embodiments, types of compounds include, but are notlimited to, peptide analogs including peptides comprising non-naturallyoccurring amino acids, e.g., D-amino acids, phosphorous analogs of aminoacids, such as .alpha.-amino phosphoric acids and α-amino phosphoricacids, or amino acids having non-peptide linkages, nucleic acid analogssuch as phosphorothioates and PNAs, hormones, antigens, synthetic ornaturally occurring drugs, opiates, dopamine, serotonin, catecholamines,thrombin, acetylcholine, prostaglandins, organic molecules, pheromones,adenosine, sucrose, glucose, lactose and galactose. Libraries ofpolypeptides or proteins can also be used.

In a preferred embodiment, the combinatorial libraries are small organicmolecule libraries, such as, but not limited to, benzodiazepines,isoprenoids, thiazolidinones, metathiazanones, pyrrolidines, morpholinocompounds, and diazepindiones. In another embodiment, the combinatoriallibraries comprise peptoids; random bio-oligomers; benzodiazepines;diversomers such as hydantoins, benzodiazepines and dipeptides;vinylogous polypeptides; nonpeptidal peptidomimetics; oligocarbamates;peptidyl phosphonates; peptide nucleic acid libraries; antibodylibraries; or carbohydrate libraries. Combinatorial libraries arethemselves commercially available (see, e.g., Advanced ChemTech EuropeLtd., Cambridgeshire, UK; ASINEX, Moscow Russia; BioFocus plc,Sittingbourne, UK; Bionet Research (A division of Key Organics Limited),Camelford, UK; ChemBridge Corporation, San Diego, Calif.; ChemDiv Inc,San Diego, Calif.; ChemRx Advanced Technologies, South San Francisco,Calif.; ComGenex Inc., Budapest, Hungary; Evotec OAI Ltd, Abingdon, UK;IF LAB Ltd., Kiev, Ukraine; Maybridge plc, Cornwall, UK; PharmaCore,Inc., North. Carolina; SIDDCO Inc, Tucson, Ariz.; TimTec Inc, Newark,Del.; Tripos Receptor Research Ltd, Bude, UK; Toslab, Ekaterinburg,Russia).

In one embodiment, the combinatorial compound library for the methods ofthe present invention may be synthesized. Combinatorial compoundlibraries useful for the methods of the present invention can besynthesized on solid supports. As used herein, the term “solid support”is not limited to a specific type of solid support. Rather a largenumber of supports are available and are known to one skilled in theart. Solid supports include silica gels, resins, derivatized plasticfilms, glass beads, cotton, plastic beads, polystyrene beads, dopedpolystyrene beads (as described by Fenniri et al., 2000, J. Am. Chem.Soc. 123:8151-8152), alumina gels, and polysaccharides. A suitable solidsupport may be selected on the basis of desired end use and suitabilityfor various synthetic protocols. In some embodiments of the presentinvention, compounds can be attached to solid supports via linkers

Screening comprises contacting a labeled target protein with anindividual, or small group, of the components of the compound library,or with a large number of compounds bound on an array. Preferably, thecontacting occurs in an aqueous solution, and most preferably, underphysiologic conditions. The aqueous solution preferably stabilizes thelabeled target nucleic acid and prevents denaturation or degradation ofthe nucleic acid without interfering with binding of the compounds.

Generally, it is convenient to test the libraries using a one well-onecompound approach to identify compounds which compete with the peptidefusion protein or high affinity peptide for binding to the receptor. Asingle compound per well can be used, at about 1 microM each or at anyconvenient concentration depending on the affinity of the receptor forthe compounds and the peptide against which they are being tested.Compounds may be pooled for testing, however this approach requiresdeconvalution. Gilchrist, U.S. Pat. No. 7,294,472.

Assays to Test CFHR1 and CFHR3 Activity

Any assay for determining the biological activity of the targetedproteins. One such assay is a hemolysis assay. CFHR3, CFHR1 will beadded to CFHR3/CFHR1-depleted plasma (30%) or to serum derived frompatients with deletions of the CFHR1 and CHFR3 genes. Serum will beincubated at 37° C. for 20 min with about 2×107 sheep erythrocytes inactivation buffer C (20 mm Hepes, 144 mm NaCl, 7 mm MgCl2, 10 mm EGTA,pH 7.4). Supernatants will be recorded at 415 nm. Generation ofcomplement activation products C3a and C5a will be followed by ELISA.Increasing amounts of CFHR3 (0.02-1 μm), CFHR1 (0.02-1.16 μm), willadded to CFHR1- and CFHR3-deficient plasma from a healthy donor (20%)and incubated for 20 min at 37° C. with chicken erythrocytes (5×106)which are more sensitive to hemolysis than sheep erythrocytes.Supernatants will be recorded at 415 nm. These assays will be repeatedin the presence of inhibitory compounds to test their activity andspecificity in inhibiting CFHR3, CFHR1, or factor H activity.

In inhibition of the ability of CFHR1 and CFHR3 and CFH to regulate C3convertase by a candidate compound in a screen involves generating C3convertase by incubation of C3b (5 μg/ml) and C3 (50 μg/ml) with factorD (2.5 μg/ml), properdin (2.5 μg/ml) and factor B (5 μg/ml) inactivation buffer C (20 mm Hepes, 144 mm NaCl, 7 mm MgCl2, 2 mm NiCl2,10 mm EGTA, pH 7.4). Activity of C3 convertase will be measured by C3ageneration after incubation of constant amounts of C3 (50 μg/ml) andincreasing concentrations of CFHR1, CFHR3 (25 and 50 μg/ml, 0.5 and 1μm). C3a concentrations can be determined by ELISA (Quidel, USA). Forthe determination of C3 cleavage in plasma, ΔCFHR3/CFHR1 plasma (10%)will be incubated with 20 or 40 μg/ml of CFHR3 (0.4 or 0.8 μm), CFHR1(0.46 or 0.93 μm), factor H or BSA in complement activation buffer C.C3a generation was followed by western blot analysis.

In competition assays, C3b (5 μg/ml) will be immobilized to the surfaceof a microtiter plate (nunc maxisorb) and incubated with increasingconcentrations of 1-30 μg/ml of CFHR3 (0.02-0.6 μm) or CFHR1 (0.02-0.69μm) or BSA, or CFHR1 plus CFHR3 (each 1 to 20 μg/ml, 0.02-0.46 μm).Binding of the proteins to C3b can be analyzed by flow cytometry.

EXAMPLES Example 1 A Genome-Wide Association Study of IgA Nephropathy(IgAN)

A genome-wide association study of IgA nephropathy (IgAN) was conductedin 1,194 cases and 902 controls of Chinese Han ancestry, with targetedfollow-up in Chinese and European cohorts comprising 1,950 cases and1,920 controls. Three independent loci in the major histocompatibilitycomplex (MHC), a common deletion of CFHR1 and CFHR3 at Chr. 1q32 and alocus at Chr. 22q12 that each surpassed genome-wide significance(p-values for association between 1.59×10⁻²⁶ and 4.84×10⁻⁹ and minorallele odds ratios of 0.63-0.80) were identified. These five lociexplain 4-7% of the disease variance and up to a 10-fold variation ininterindividual risk. Many of the IgAN-protective alleles are known toimpart increased risk of other autoimmune or infectious diseases, andIgAN protective allele frequencies closely parallel the variation indisease prevalence among Asian, European and African populations,suggesting complex selective pressures (all 10 protective alleles areidentified in Table 2).

Study Design and Genotyping of Discovery Cohort.

To detect loci conferring susceptibility to IgAN, a two-stage GWAS wasperformed (Table 1). In the discovery phase, genome-wide genotyping wasperformed on the Illumina 610 quad platform in 1,228 biopsy-proven IgANcases and 966 healthy controls of Chinese Han ancestry recruited fromBeijing (Table 1). The top signals in the discovery phase were furtherevaluated in an independent cohort of Han Chinese descent (Shanghaicohort, 740 cases and 750 controls) and a European cohort of Italian andNorth American origin (combined by stratified analysis, 1,273 cases and1,201 controls). Subsequently, the Beijing, Shanghai and Europeancohorts were analyzed together to identify genome-wide significant loci.

Genome-Wide Association Analysis.

Stringent quality control filters were applied in the analysis ofgenome-wide genotyping data that resulted in elimination of 5% of thesamples due to low call rate, duplication, cryptic relatedness or gendermismatch and 16.8% of markers primarily due to low minor allelicfrequency (<0.01). After quality control, the genotyping call rate was0.9992. The standard 1-degree of freedom Cochran Armitrage (CA) trendtest was used to analyze 498,322 SNPs in the discovery cohort of 1,194cases (650 males/544 females, average age 31.1 years) and 902 controls(608 males/294 females, average age 31.5 years). The quantile-quantileplot showed no global departure from the expected distribution ofp-values and the inflation factor (λ) was 1.024, indicating negligiblepopulation stratification. Accordingly, principal component analysis(PCA) demonstrated that cases and controls were matched along the axesof significant principal components, and PCA correction did notsubstantially change the distribution of the association statistic orthe genomic inflation factor (λ=1.022). Analysis indicated that theassociation results were not biased by differences in ancestry orpopulation structure between cases and controls.

The genome-wide association analysis revealed 27 SNPs exceedinggenome-wide thresholds for significance (p≦5×10⁻⁸). These 27 signals allresided in a 0.54 Mb interval within the major histocompatibilitycomplex (MHC) on Chr. 6p21, with the top signal at rs9275596(p=1.9×10⁻¹²). Interestingly, fourteen MHC SNPs with suggestive p-values(5×10⁻⁶ to 1×10⁻⁴) showed little or no linkage disequilibrium withrs9275596 (FIG. 1a ).

Follow-Up of Top Signals from Discovery Stage

After removal of MHC SNPs, additional loci showing departure from theexpected p-value distribution remained. Signals based on the falsediscovery rate were ranked and it was decided to follow-up loci withp-value≦1.3×10⁻⁵, corresponding to a q-value≦0.10.²⁰. Power calculationsindicated that this strategy provides 80% power to detect loci withallelic frequencies>0.10 and relative risk>1.5 with genome-widesignificance (p<5×10⁻⁸) in the combined cohort. In total, 65 SNPs from10 distinct loci met these criteria (including three potentiallyindependent loci in the MHC and two in the Chr. 22q12.2 interval). Thetop-scoring SNP's and one additional SNP from each of these intervalswere genotyped in follow-up cohorts (total 20 SNPs in 3,870 individualsafter quality control, table 1). Tests of association were performedwithin each cohort, followed by a combined analysis with the discoverycohort using Mantel's extension of CA trend test (Table 2).

Five of the ten loci selected for follow-up surpassed the threshold forsignificant genome-wide association: three loci within 6p21, one locusat 1q32, and one locus at 22q12.2 (Table 2,). Each signal demonstratedsignificant association with consistent effect size for the sameprotective allele in each individual cohort, with little evidence forheterogeneity.

The strongest association in the combined cohort was a locus defined byrs9275596 within a ˜170 kb interval that includes the HLA-DRB1, -DQA1,and -DQB1 genes (rs9275596), OR=0.63, p=1.6×10⁻²⁶). SNP (rs9275596)achieves genome-wide significance with a consistent effect size in eachcohort (Table 2, FIG. 1b ) and has strong supporting association from anearby SNP in strong LD (rs2856717). However, SNP (rs9275596) by itselfdid not explain all of the signal at 6p21.

Conditioning for the effect of rs9275596 eliminated evidence ofassociation for the majority of SNPs in close proximity to 6p21 howevertwo distinct loci maintained genome-wide significance. The secondindependent locus is defined by rs9357155 (which has an r²=0.01 withrs9275596 in the combined cohort) and shows an OR=0.74 and a p-value of6.9×10⁻⁹ for association with IgAN after conditional analysis (Table 3,FIG. 1c ). SNP rs9357155 lies in a ˜100 kb segment of LD and lies 128 kbcentromeric to rs9275596. This LD segment contains the genes TAP2, TAP1,PSMB8, and PSMB9, and the supporting SNPs in this region (rs2071543) isa missense variant in PSMB8 (Q49K) that is at a position completelyconserved among all orthologs (most distantly related ortholog is inplatypus; Tables 2 and 3, FIG. 1c .).

After conditioning for the effects of both rs9275596 and rs9357155, athird locus within the MHC defined by rs1883414, which lies 400 kbcentromeric to rs9275596 and shows r²=0.005 and 0.002 with rs9275596 andrs9357155, respectively, also shows a conditioned OR of 0.77 and p-valueof 3.1×10⁻⁸ for association (Table 3). This signal in theHLA-DPA1-DPB1-DPB2 region is supported by a second locus defined by SNP(rs3129269) and demonstrated consistent effect size across cohorts(Tables 2, 3, FIG. 1d ).

To better delineate the risk associated with the MHC region and detectpotential functional variants, classical HLA alleles were imputed in thediscovery cohort.²¹ This demonstrated a genome-wide significantassociation with a protein-altering variant of known functionalsignificance, the DQB1*0602 allele (OR=0.47, p=6.6×10⁻⁹). DQB1*602 is instrong LD with another functional allele, DRB1*1501. However,conditional analysis showed that DQB1*602 best explained thisassociation signal. The strength of the DQB1*602 association is probablyunderestimated due to the limitations of current imputation algorithms(sensitivity of 56.6% for detection of the DQB1*602 allele).

A major signal outside the MHC locus resided in a 100-kb segment on Chr.1q31-q32.1 containing complement factor H (CFH) and the related CFHR3,CFHR1, CFHR4, CHFR2, CFHR5 genes (rs6677604, OR=0.68, p=3.0×10⁻¹⁰ in thecombined cohort). This locus defined by rs6677604 was also the topsignal in the genome-wide CNP analysis. SNP rs6677604, is located inintron 12 of CFH and is supported by multiple highly correlated SNPs(FIG. 2a , Table 2). After controlling for rs6677604, there were noother independent signals in the entire CFH region. The associationresults at rs6677604 were far less significant under a recessive model(p=5.6×10⁻⁵), which supports an additive risk. The rs6677604-A allele isprotective in all three cohorts but has a much higher allele frequencyin Europeans (0.23 in European controls vs. 0.07 in Chinese controls,Table 2). This allele perfectly tags a common deletion spanning theCFHR1 and CFHR3 genes (CFHR1,3Δ)^(22,23). The association of rs6677604-Aallele with CFHR1,3Δ in the cohort tested was confirmed: PCR of multipleamplicons within CFHR1 and CFHR3 failed and the CFHR1 protein could notbe detected in serum from all A/A homozygotes tested. Evidence forassociation of IgAN with alleles in CFH that confer risk of maculardegeneration (AMD) was carefully evaluated and no contribution to riskwas found (e.g., the Y402H variant, tagged by rs10801555, showed OR=1.0,p=0.99 in discovery cohort; FIG. 2b ). Haplotype-based analysis in theBeijing discovery cohort demonstrated protection by the haplotypecontaining the rs6677604-A allele (OR=0.56, p=1×10⁻⁶ vs. all otherhaplotypes in the discovery cohort, FIG. 2b ) but no significant effectof other haplotypes.

The fifth signal in the GWAS resided at a locus in an intronic SNP inHORMAD2 on Chr. 22.q12.2 defined by rs2412971 (OR=0.80, p=1.9×10⁻⁹) andwas supported by a second SNP within 35 kb of this signal (rs2412973,OR=0.80, p=4.5×10⁻⁹). After controlling for rs2412971, there were noother independent signals in this region. The association extendedacross a large LD segment that encompasses genes including HORMAD2,MTMR3, LIF, and OSM (FIG. 2c ).

Cumulative Effects on Disease Risk.

To determine the cumulative risk conferred by these loci, a genetic riskscore was computed, calculated as the weighted sum of the number ofindependent protective alleles multiplied by the log of the odds ratiofor each of the individual loci (Table 4). The disease risk varied up to10-fold between individuals with no protective alleles compared thosewith five or more. The risk score model was similar in all cohorts andcollectively explained 5-7% of the variation in disease risk in theChinese cohorts and ˜4% of the risk in the European cohort (Table 4).The risk score did not reproducibly correlate with any of the parametersof disease severity, such as estimated GFR, degree of proteinuria, orhistologic severity grade.

Most interestingly, consistent with the known higher prevalence of IgANin Asians, the frequency of protective alleles was significantly lowerin the Chinese [Beijing and Shanghai cohorts] cohorts compared to theEuropean group. The differences in the distribution of protectivealleles were highly significant between the Asian and European cohorts(p=4.8×10⁻⁷² and p=6.4×10⁻⁶⁰ for differences within cases and controls,respectively). To confirm this finding in independent populations, threeHapMap groups were examined and it was similarly found that frequenciesof protective alleles correlated with disease frequency among thesepopulations: protective allele frequencies were highest in Asians,intermediate in Europeans, and lowest in Africans. For example, theprotective allele at the chromosome 1 locus showed a frequency of 0.08in Asians, 0.24 in Europeans and 0.49 in Africans.

These five risk loci explained up to a ten-fold variation ininterindividual risk and cumulatively accounted for 4-7% of the diseasevariance. The effect sizes at these loci are relatively large andconsistent across the European and Chinese cohorts, with four havinginverse OR≧1.4, which is comparable to those detected in previousstudies of autoimmune or inflammatory diseases^(21,24-30). Theprotective allele frequencies also strongly paralleled the prevalence ofIgAN among different populations.

There was a major signal in the MHC region, which was identified but notlocalized in a recent GWAS with 533 affected subjects¹⁹. The studyherein revealed that this signal originated from three distinct lociwithin HLA and two additional non-HLA loci. Evidence supporting thepresence of three independent risk loci on Chr. 6p21 includes theirposition within distinct LD segments, as well as genome-widesignificance after conditioning for the other two loci, with consistenteffects within each cohort.

The strongest HLA signal was in the HLA-DRB1/DQB1 region. Imputation ofclassical alleles suggested that this signal is fully or partiallyconveyed by a strong protective effect of the DRB1*1501-DQB1*0602haplotype; the strength of this association was likely underestimated bylimitations of imputation. This haplotype is relatively common in theEuropean and Asian populations (frequency˜0.1-0.2) and in contrast toits protective effect for IgAN has been associated with increased riskof SLE²⁵, multiple sclerosis³¹, narcolepsy³² and hepatotoxicity fromCOX2 inhibitors³⁰ but is also highly protective for type I diabetesmellitus²⁶. This haplotype is also protective in selective IgAdeficiency²⁷, however there was no association with IgA levels at thislocus among the examined cases. This region has a complex LD structure,and the conditional analysis used in these studies suggests thepossibility of an independent signal within this region (at rs9275424).

The second independent interval at 6p21 contained TAP2, TAP1, PSMB8, andPSMB9, interferon-regulated genes that have been implicated in antigengeneration and processing for presentation by MHC I molecules; they alsoplay an important role in modulation of cytokine production andcytotoxic T-cell response^(33,34). PSMB8 expression is increased inPBMCs from IgAN subjects³⁵. This locus has not been identified in anyprior GWAS.

The third signal at 6p21 comprised the HLA-DPA1, -DPB1, and -DPB2 genes.This locus is associated with risk of chronic hepatitis B infection²⁹ (amajor clinical problem in China) and systemic sclerosis stratified foranti-DNA topoisomerase I or anticentromere autoantibodies³¹, but theprotective alleles associated with these phenotypes are not in LD withany of the IgAN protective alleles.

The results show unambiguous protective effect of theCFHR1,3Δ-containing haplotype in IgAN, strongly suggesting that CFHR1,3Δis the functional variant. It is not clear how loss of CFHR1 and/orCFHR3 may confer protection for IgAN. Without being bound by theory, theprotective effects may be due to the competing roles of CFH and CFHR1proteins³⁷, such that loss of CFHR1 enhances CFH effects, reducinginflammation at tissue surfaces.

The Chr. 22q12.2 locus spans a large interval that contains OSM and LIF,encoding cytokines implicated in mucosal immunity and inflammation Thers2412973-A allele, which is protective for IgAN, has also beenassociated with increased risk of early-onset inflammatory bowel disease(IBD) and altered expression of MTMR3 expression in individuals withulcerative colitis²⁸. This finding is of interest given the knownclinical association between IBD and secondary forms of IgAN. Lastly,the protective allele at this locus is also associated with lower serumIgA levels among cases (p=3.9×10⁻³).

It is noteworthy that many of the protective alleles for IgAN have beenimplicated as risk factors other immune-mediated and infectiousdisorders, demonstrating that complex selection pressures (potentiallybalancing selection) influence the frequencies of these alleles amongworld populations.

Materials and Methods

Genome-wide genotyping, genotype quality control, and primaryassociation analyses for these studies are described in REF Gharavi A G,Kiryluk K, Choi M, Li Y, Hou P, et al. (2011) Genome-wide associationstudy identifies susceptibility loci for IgA nephropathy. Nat Genet.After quality control analysis, the discovery cohort consisted of 1,194cases and 902 controls genotyped with the Illumina Human 610-QuadBeadChip. The primary genome-wide association analyses were performedusing PLINK v1.07⁴⁴. A standard 1-df Cochran-Armitage trend test was theprimary association test, as it demonstrates greater robustness todeviations from Hardy-Weinberg equilibrium compared to its alternatives.The per-allele odds ratios were estimated and 95% confidence intervalsfor all tested SNPs. The genome-wide distributions of p-values wereexamined using qq-plots, before and after exclusion of the HLA region.

FDR and Power Analysis:

The calculation of positive false discovery rate (pFDR) was performedusing the q-value package (R). The proportion of SNPs that were trulynull (Πo) was estimated at 0.991 using the empiric distribution ofgenome-wide p-values²⁰. The q-value of 0.10 (positive FDR of 10%)corresponded to the p-value of 1.3×10⁻⁵. This q-value threshold defined65 top SNPs that were subsequently analyzed for replication. The poweranalysis was performed using methods developed by Skol et al⁴⁵. Thecalculations were performed under the following assumptions: diseaseprevalence 1%, additive risk model, stage I (discovery) sample size of1000 cases and 1000 controls, stage II (follow-up) sample size of 2000cases and 2000 controls, follow-up significance threshold of 1.3×10⁻⁵,and joint (stage I and II) significance level of 5×10⁻⁸. The joint powerof our study design was calculated for a range of disease allelefrequencies (0.10-0.50) and effect sizes (GRR 1.10-1.80). The effectsizes that are detectable at alpha 5×10⁻⁸ and power 0.80 in the jointanalysis were estimated using CaTS software⁴⁵.

Selection of SNPs for Follow-Up:

The 65 SNPs that reached our q-value threshold were first clustered into10 distinct loci based on their physical location and regional patternsof LD. The correctness of genotype calls was verified for each SNPindividually, by visual inspection of the Illumina cluster plots.Conditional logistic regression analysis was performed to confirmcorrect SNP grouping and detect independence signals. These analysessuggested 3 distinct loci on chromosome 6p21 and 2 distinct loci onchromosome. 22q12.2. The SNP's with the lowest p-value within each locuswas selected for follow-up. The selection of the second SNP for back-upgenotyping was based mainly on its strength of association, high LD withthe top-scoring SNP in European and Chinese HapMap populations,robustness of Illumina clustering plots, and high genotyping rate. Intotal, we selected 20 representative SNPs for genotyping in 2,013 casesand 1,951 controls recruited for stage 2 of the study.

Association Analyses Across Multiple Cohorts:

Result across multiple cohorts were combined using a stratified trendtest with Mentel's extension of the Cochran-Armitage test (SNPMatrixpackage, R)⁴⁶. Heterogeneity across cohorts was tested with theheterogeneity index (I²), and by performing Cochrane's Q heterogeneitytest. In order to ensure findings were robust to methodology, we alsocombined the per-allele effect estimates using Cochran-Mantel-Haenszelstratified analysis, as well as an inverse variance-weighted methodunder a fixed-effects model. The results were concordant regardless ofthe meta-analytic method used.

Conditional Analyses:

A stepwise logistic regression was performed after controlling for thegenotypes of the conditioning SNPs using PLINK (v1.07). The adjusted(conditioned) effect estimates were then combined across cohorts byadding cohort information as an additional covariate in the stratifiedanalysis (Table 3). Similar approach was used for the conditionalanalysis of classical HLA alleles.

Haplotype-Based Association at CFH Locus:

These analyses were performed in PLINK v.1.07. Haplotypes were phasedacross the CFH locus in the Beijing cohort (FIG. 2b ) and haplotypefrequencies were estimated in the cases and controls separately, as wellas jointly in the entire cohort. Only the haplotypes with the overallfrequency greater than 1% were included in association analyses. Thep-values were derived for tests of association of one haplotype versusall others. The odds ratios and the corresponding 95% confidenceintervals were estimated in reference to the AMD risk haplotype (H-1,FIG. 2b ) which has an identical frequency between cases and controls.

Imputation and Association Analysis of Classical HLA Alleles

The HLA classical alleles at DQB1, DQA1 and DRB1 loci were imputed basedon the genotype data from the Beijing cohort. In short, the genotypedata were first phased using BEAGLE⁴⁷ and pairwise IBD status wasdetermined using the GERMLINE software⁴⁸. The HLA classical allelestatus and genotype data of the HapMap Han Chinese individuals wereutilized as a reference panel²¹ using the HLA-via-IBD software. Theaccuracy of the imputation procedure was tested by direct sequencing ofthe informative coding segments of HLA-DQB1 gene in a random subset of420 samples. This demonstrated that imputation had 57% sensitivity and96% specificity for identifying the HLA-DQB1*602 alleles.

Risk Score Discovery and Validation:

Among the 5 independent regions of association, alleles with lowerfrequency conveyed a protective effect. Therefore, the risk score modelwas based on protective alleles for the top five independent and moststrongly associated SNPs (rs9275596, rs9357155, rs1883414, rs2412971,and rs6677604). A Risk Score can also be determined based on the fiveredundant alleles. The Risk Score was calculated as a weighted sum ofthe number of protective alleles at each locus multiplied by the log ofthe odds ratio for each of the individual loci for a specific cohort.Only individuals with non-missing genotypes for all 10 alleles wereincluded in this analysis (Table 4). The same method is used forcalculating a risk score that includes the two protective allelesdiscovered in the second study described in Example 2.

The predictive risk score models were built using association resultsfor each of the three model-building cohorts and were validated bytesting their predictive properties against all other cohorts (targetcohorts). The percent of the total variance in disease state explainedby the risk score was estimated by Nagelkerke's pseudo R-squared fromthe logistic regression model with the risk score as a quantitativepredictor and disease state as an outcome. The C-statistic was estimatedas an area under the ROC curve provided by the above logistic model.These analyses were performed with SPSS Statistics version 17.0.

Distributions of Protective Alleles:

Each individual study participant was scored for the number ofprotective alleles and the distributions of protective alleles werecompared between various ethnic groups. Only individuals with completegenotype information were included. Because relatively few individualshad 5 or more protective alleles, they were binned into a singlecategory for the purpose of statistical testing and a chi-squaregoodness-of-fit test was used to derive p-values. Analysis of the HapMaprelease 23 dataset included 30 unrelated individuals from Yoruba inIbadan, Nigeria (YRI), 30 unrelated Utah residents with ancestry fromnorthern and western Europe (CEU), and a combined group of 45 unrelatedJapanese individuals from Tokyo, Japan (JPT) and 45 Han Chinese fromBeijing, China (CHB). The genotype data were downloaded directly fromthe HapMap Project website.

Common Copy Number Polymorphisms (CNP) Analysis:

For the purpose of this analysis, publicly available CNP discovery datawere used that were obtained with 2.1-million NimbleGen CGH arrays byConrad et al^(49,50). 1,051 SNPs present on the Illumina HumanHap 610Kchip that tag known common (>1%) CNVs at r²>0.8. The genotypes for theseSNPs were extracted from the dataset and analyzed separately forassociation with the disease state. These SNPs underwent all QC steps asoutlined above prior to association analysis. A simple 1-df chi-sqallelic test was used to screen for association (PLINK) and the resultswere ranked and visualized using a quantile-quantile plot (R). The topassociated CNPs were validated using quantitative real-time PCR.

Quantitative Real-Time PCR:

qPCR was performed on genomic DNA using the iQ5 Real-Time PCR DetectionSystem (Bio-Rad) and amplification was achieved using SYBR GreenSupermix (Bio-Rad) with a standard 2-step amplification protocol. Allsamples were analyzed in triplicates. Three amplicons spanning CFHR1 andCFHR3 were tested and the signal was normalized to an amplicon inB-actin. Pooled DNA from 10 individuals homozygous for G alleles atrs6677604 was used as reference.

Western Blotting:

Diluted plasma samples were separated on 4-15% Ready Gel (Bio-Rad,Hercules, Calif.), transferred to PVDF Membranes (Millipore, Billerica,Mass.), and protein blotted with primary antibodies against CFH (AbDSerotec, Raleigh, N.C.) and CFHR1 (R&D Systems, Minneapolis, Minn.)using standard protocols.

Genome-Wide Genotyping, Genotype Quality Control, and PrimaryAssociation Analyses:

After quality control analysis, the discovery cohort consisted of 1,194cases and 902 controls genotyped with the Illumina Human 610-QuadBeadChip. The primary genome-wide association analyses were performedusing PLINK v1.07¹. A standard 1-df Cochran-Armitage trend test was usedas the primary association test, as it demonstrates greater robustnessto deviations from Hardy-Weinberg equilibrium compared to itsalternatives. The per-allele odds ratios and 95% confidence intervalswere estimated for all tested SNPs. The genome-wide distributions ofp-values were examined using qq-plots, before and after exclusion of theHLA region.

FDR and Power Analysis:

The calculation of positive false discovery rate (pFDR) was performedusing the q-value package (R). The proportion of SNPs that were trulynull (Πo) was estimated at 0.991 using the empiric distribution ofgenome-wide p-values². The q-value of 0.10 (positive FDR of 10%)corresponded to the p-value of 1.3×10⁻⁵. This q-value threshold defined65 top SNPs that were subsequently analyzed for replication. The poweranalysis was performed using methods developed by Skol et al³. Thecalculations were performed under the following assumptions: diseaseprevalence 1%, additive risk model, stage I (discovery) sample size of1000 cases and 1000 controls, stage II (follow-up) sample size of 2000cases and 2000 controls, follow-up significance threshold of 1.3×10⁻⁵,and joint (stage I and II) significance level of 5×10⁻⁸. The joint powerof our study design. was calculated for a range of disease allelefrequencies (0.10-0.50) and effect sizes (GRR 1.10-1.80). The effectsizes that are detectable at alpha 5×10⁻⁸ and power 0.80 in the jointanalysis were estimated using CaTS software³.

Selection of SNPs for Follow-Up:

The 65 SNPs that reached our q-value threshold were first clustered into10 distinct loci based on their physical location and regional patternsof LD. The correctness of genotype calls was verified for each SNPindividually, by visual inspection of the Illumina cluster plots.Conditional logistic regression analysis was performed to confirmcorrect SNP grouping and detect independence signals. These analysessuggested 3 distinct loci on chromosome. 6p21 and 2 distinct loci onchromosome. 22q12.2. The SNP's with the lowest p-value within each locuswas selected for follow-up. The selection of the second SNP for back-upgenotyping was based mainly on its strength of association, high LD withthe top-scoring SNP in European and Chinese HapMap populations,robustness of Illumina clustering plots, and high genotyping rate. Intotal, 20 representative SNPs were selected for genotyping in 2,013cases and 1,951 controls recruited for stage 2 of the study.

Association Analyses Across Multiple Cohorts:

Result across multiple cohorts were combined using a stratified trendtest with Mentel's extension of the Cochran-Armitage test (SNPMatrixpackage, R)⁴. Heterogeneity was tested across cohorts with theheterogeneity index (I²), and by performing Cochrane's Q heterogeneitytest. In order to ensure findings were robust to methodology, theper-allele effect estimates were also combined usingCochran-Mantel-Haenszel stratified analysis, as well as an inversevariance-weighted method under a fixed-effects model. The results wereconcordant regardless of the meta-analytic method used.

Conditional Analyses:

Stepwise logistic regression was performed after controlling for thegenotypes of the conditioning SNPs using PLINK (v1.07). The adjusted(conditioned) effect estimates were then combined across cohorts byadding cohort information as an additional covariate in the stratifiedanalysis (Table 3). Similar approach was used for the conditionalanalysis of classical HLA alleles.

Haplotype-Based Association at CFH Locus:

The genotype data of the Beijing cohort was extracted and the haplotypeswere phased across the CFH locus (FIG. 2b ). The haplotype frequencieswere estimated in the cases and controls separately, as well as jointlyin the entire cohort. Only the haplotypes with the overall frequencygreater than 1% were included in association analyses. The p-values werederived for tests of association of one haplotype versus all others. Theodds ratios and the corresponding 95% confidence intervals wereestimated in reference to the AMD risk haplotype (H-1, FIG. 2b ) whichhas an identical frequency between cases and controls. These analyseswere performed in PLINK v.1.07.

Imputation and Association Analysis of Classical HLA Alleles:

The HLA classical alleles at DQB1, DQA1 and DRB1 loci were imputed basedon the genotype data from the Beijing cohort. In short, the genotypedata was first phased using BEAGLE⁵ and pairwise IBD status wasdetermined using the GERMLINE software⁶. The HLA classical allele statusand genotype data of the HapMap Han Chinese individuals was utilized asa reference panel⁷. The imputation was performed using the HLA-via-IBDsoftware. The accuracy of the imputation procedure was tested by directsequencing of the informative coding segments of HLA-DQB1 gene in arandom subset of 420 samples. This demonstrated that imputation had 57%sensitivity and 96% specificity for identifying the HLA-DQB1*602alleles.

Risk Score Discovery and Validation:

Among the 5 independent regions of association, alleles with lowerfrequency conveyed a protective effect. Therefore, the risk score modelwas based on protective alleles for the top five independent and moststrongly associated SNPs (rs9275596, rs9357155, rs1883414, rs2412971,and rs6677604). The Risk Score was calculated as a weighted sum of thenumber of protective alleles at each locus multiplied by the log of theodds ratio for each of the individual loci for a specific cohort. Onlyindividuals with non-missing genotypes for all 10 alleles were includedin this analysis (Table 4). The predictive risk score models were builtusing association results for each of the three model-building cohortsand were validated by testing their predictive properties against allother cohorts (target cohorts). The percent of the total variance indisease state explained by the risk score was estimated by Nagelkerke'spseudo R-squared from the logistic regression model with the risk scoreas a quantitative predictor and disease state as an outcome. TheC-statistic was estimated as an area under the ROC curve provided by theabove logistic model. These analyses were performed with SPSS Statisticsversion 17.0.

Distributions of Protective Alleles:

Each individual study participant was scored for the number ofprotective alleles and the distributions of protective alleles werecompared between various ethnic groups. Only individuals with completegenotype information were included. Because relatively small number ofindividuals had 5 or more protective alleles, they were binned into asingle category for the purpose of statistical testing and a chi-sqgoodness-of-fit test was used to derive p-values. Analysis of the HapMaprelease 23 dataset included 30 unrelated individuals from Yoruba inIbadan, Nigeria (YRI), 30 unrelated Utah residents with ancestry fromnorthern and western Europe (CEU), and a combined group of 45 unrelatedJapanese individuals from Tokyo, Japan (JPT) and 45 Han Chinese fromBeijing, China (CHB). The genotype data was downloaded directly from theHapMap Project website.

Common Copy Number Polymorphisms (CNP) Analysis:

For the purpose of this analysis, we utilized publicly available CNPdiscovery data obtained with 2.1-million NimbleGen CGH arrays by Conradet al^(8,9). 1,051 SNPs were present on the Illumina HumanHap 610K chipthat tag known common (>1%) CNVs at r²>0.8. The genotypes for these SNPswere extracted from the dataset and analyzed separately for associationwith the disease state. These SNPs underwent all QC steps as outlinedabove prior to association analysis. A simple 1-df chi-sq allelic testwas used to screen for association (PLINK) and the results were rankedand visualized using a quantile-quantile plot (R). The top associatedCNPs were validated using quantitative real-time PCR.

Quantitative Real-Time PCR:

qPCR was performed on genomic DNA using the iQ5 Real-Time PCR DetectionSystem (Bio-Rad) and amplification was achieved using SYBR GreenSupermix (Bio-Rad) with a standard 2-step amplification protocol. Allsamples were analyzed in triplicates. Three amplicons spanning the CFHR1and CFHR3 were tested and the signal was normalized to an amplicon inB-actin. Pooled DNA from 10 individuals homozygous for G alleles atrs6677604 was used as reference.

Western Blotting:

Diluted plasma samples were separated on 4-15% Ready Gel (Bio-rad),transferred to PVDF Membranes (Millipore), and protein blotted withprimary antibodies against CFH (AbD Serotec) and CFHR1 (R&D Systems)using standard protocols.

Example 2 Eight Cohort Replication Study

Replication Study Methods:

For replications eight cohorts (five European, two East Asian, and oneAfrican-American cohort, totaling 2,228 cases and 2,561 controls wereexamined. While each individual cohort at best had 40-50% power toreplicate original GWAS findings, the combined replication cohort (2,228cases and 2,561 controls) provided essentially 100% power forreplication across the range of alleles frequencies and odds ratiosinitially observed.

The two top-scoring SNPs were genotyped for the CFHR3/R1, TAP2/PSMB9,DPA1/DPB2, and HORMAD2 loci, but four SNPs were included for theDQB1/DRB1 locus to test for independent alleles at this interval byconditional analysis. After a standard assessment of genotype qualitycontrol, association testing was performed within each cohort using thestandard Cochrane-Armitage trend test. Testing was also done forheterogeneity of associations and performed a meta-analysis under bothfixed and random effects models (Table 5).

Four of the five original GWAS loci displayed significant replicationwith direction-consistent ORs and no heterogeneity comparable to theoriginal findings (Table 5). The strongest replication was at theDQB1/DRB1 locus and achieved genome-wide significance in the replicationcohort (fixed effects OR 0.75, P-value 4×10⁻¹¹). The CFHFR3/R1 locus onChr.1q32, the HORMAD2 locus on Chr.22q12, and the DPA1/DPB2 locus onChr.6p21 were also robustly replicated (fixed effects p-values3×10⁻³-7×10⁻⁷), with minimal between-cohort heterogeneity (I²<25%).Accordingly, when combined with the four cohorts studied in the originalGWAS, these four loci provided highly significant evidence ofassociation (fixed effects p-values 3×10⁻¹⁰-5×10⁻³²).

In contrast, the TAP2/PSMB9 locus on Chr. 6p21 displayeddirection-consistent replication only in the Italian, German, Czech, andJapanese cohort but the full replication cohort did not support thisassociation (Tables 1 and 9). However, when combined with the fourcohorts from the original GWAS, this locus remained genome-widesignificant (fixed effects p-values 1×10⁻⁸ and 6×10⁻¹⁰ for rs9357155 andrs2071543, respectively Table 5). As expected, I² and Q-tests providedevidence of heterogeneity and random effects meta-analysis, whichexplicitly models heterogeneity, was 1-3 orders of magnitude moresignificant than fixed effect meta-analysis at this interval (e.g.random effects p-value 3×10⁻¹¹, I²=61% for rs9357155 Table 5). Theheterogeneity was not attributable to differences in ethnicity or cohortsize as the association results varied within Asian and European cohortsof differing size. Table 9.

Conditional Analysis Reveals New Independent Protective alleles withinthe HLA-DQB1/DRB1 Locus:

The top signals in the original GWAS (Example 1), represented byrs9275596 and located within the DQB1/DRB1 locus, were mediated by avery strong protective effect of the DRB1*1501-DQB1*602 haplotype. TheSNPs in this interval are in incomplete LD, and conditional analyses inthis study and in an independent study of Europeans [10] had indicatedthat additional independent haplotypes also contributed to the signal.Additional SNPs that were in partial LD with rs9275596 were examined todetect potentially independent effects (rs9275224, rs2856717 andrs9275424, which had an r² of 0.09 to 0.7 with rs9275596. Table 10.

After mutually conditioning each SNP on the remaining SNPs, three of thefour SNPs in the DQB1/DRB1 region exhibited a genome-wide significantindependent effect (rs9275596, rs9275224 and rs2856717, conditionedp-vales<5×10⁻⁸. Table 6 presents replication study results and combinedmeta-analysis. Combined association results for 12 SNPs representing 5independent regions that reached genome-wide significance in theoriginal GWAS. The combined effect estimates (per allele odds ratios) inthe replication cohorts were all direction-consistent with the ones inthe original GWAS cohorts. Significant heterogeneity was noted only forthe second HLA locus represented by rs9357155 and rs2071543. Resultsshow that the conditioned effect of the minor allele of rs2856717 wasreversed compared to the crude effect estimate, suggesting that theadjustment for LD structure has uncovered a risk haplotype in thisregion (conditioned OR 1.61, p=2×10⁻¹⁰).

The above data indicated that there are multiple risk haplotypes withinthe DQB1/DRB1 locus. To better define these findings, four-SNPhaplotypes at this locus were phased and associations with disease weretested (Table 7). There was a very strong protective effect of the ATAChaplotype (freq. 0.21) which, based on our previous imputation analysis,carries the DRB1*1501/DQB1*602 classical alleles. In addition, a newprotective haplotype (ACAT, freq. 0.13) and a new risk haplotype (ATAT,freq. 0.05) were defined. The ATAC protective haplotype and the ATATrisk haplotype differ only by the rs9275596-C/T allele, explaining thereversal of OR for the rs2856717-T allele after conditioning forrs9275596 (Table 7). Additionally, the GCGT risk haplotype, tagged bythe rs9275424-G allele, exhibited a weaker protective effect. Theseresults were supported by both Asian and European cohorts. There are atleast three independent haplotypes conferring risk of IgAN within thisregion. Further support is provided by the global haplotype associationtest, which achieved a p-value of 3×10³ and thus was at least elevenorders of magnitude more significant than the individual SNP associationtests at this locus.

These three independent haplotypes in-DQB1/DRB1 locus still did notexplain associations in other Chr. 6p21 regions (TAP2/PSMB9 andDPA1/DPB2 loci, respectively represented by rs9357155 and rs1883414),and a fully adjusted model that included all independently associatedSNPs continued to support the original GWAS findings of three discretegenome-wide significant intervals on Chr. 6p21 (Table 11).

First-Order Interaction Screen Reveals Significant Interaction BetweenCFHR3/R1 and HORMAD2 Loci:

All possible pairwise interactions between the seven risk-contributingSNPs were studied (Table 12). There was strong evidence for amultiplicative interaction (defined as departure from additivity on thelog-odds scale) between the CFHR3/R1 (rs6677604) and the HORMAD2 loci(rs2412971). In this interaction, the rs2412971-A allele has a strongand consistent protective effect among all genotypic subgroups, but itseffects are reversed among homozygotes for the rs6677604-A allele, whichclosely tags a CFHR3/R1 deletion (FIG. 3, Table 12). The significance ofthis interaction (p=2.5×10⁻⁴) exceeds a Bonferroni-corrected threshold,and is most discernible among the European cohorts (p=1.4×10⁻³), whereboth SNPs have higher minor allele frequencies. The 4-df genotypicinteraction test was also significant for these two loci (p=6.4×10⁻³),but the 1-df multiplicative interaction model provided a better fit.

Seven Allele Test: Refined Genetic Risk Score

The risk score based on the five loci in the GWAS study was revised byincorporating the newly discovered independent effects of rs9275224 andrs2856717 and the interaction between the CFHR3/R1 and the HORMAD2 loci.The seven loci/protective alleles are: either rs6677604 or rs3766404],rs9275596, rs9275224, rs2856717, [either rs9357155 or rs2071543],[either rs1883414 or rs3129269] and [either rs2412971 or rs2412973].

A stepwise regression algorithm in the entire cohort defined a new riskscore that retained the seven SNPs exhibiting an independent effect aswell as the rs6677604* rs2412971 interaction term. A genetic risk scorebased on seven SNPs was more strongly associated with disease risk andexplained a greater proportion of the disease variance in both thereplication and the original GWAS (Example 1) dataset (Table 8).Moreover, the refined risk score was a highly significant predictor ofdisease in each individual replication cohort (Table 13). In alldatasets combined, the new risk score explained 4.7% in disease varianceand was 13 orders of magnitude more significant than score base on 5SNPs identified in Example 1. In this model, one standard deviationincrease in the score was associated with nearly 50% increase in theodds of disease (OR=1.47, 95% CI: 1.42-1.54, P=1.2×10⁻⁷²). Thistranslates into nearly a 5-fold increase in risk between individualsfrom the opposing extremes of the risk score distribution (with tailsdefined by ≧2 standard deviations from the mean).

Study Cohorts:

The case-control cohorts analyzed in this study were contributed byclinical nephrology centers across Europe, Asia, and North America. Allcases carried a biopsy diagnosis of IgAN defined by typical lightmicroscopy features and predominant IgA staining on kidney tissueimmunofluorescence, in the absence of liver disease or other autoimmuneconditions. Each individual cohort of cases was accompanied by a controlcohort of similar size, matched based on self-reported ethnicity andrecruited from the same clinical center.

Genotyping and Genotype Quality Control:

The genotyping was performed by KBiosciences (Hoddeston, England). andgenotype calls were determined using an automated clustering algorithmthe (SNP Viewer v.1.99, KBiosciences, 2008). The genotype clusters werealso examined visually across all plates, to assure lack of technicalartifacts. The overall genotyping rate across all samples was 98.2%. Forquality control we calculated minor allele frequencies, as well asper-SNP and per-individual rates of missingness within each case-controlcohort separately. Additionally, we tested for Hardy-Weinbergequilibrium among the control groups from each cohort to assure lack ofbias due to genotyping artifacts or population stratification. All SNPsincluded in the final analyses had minor allele frequency greater than1%, per-SNP missingness rate less than 5%, and all passed the HWE testin controls (p>1×10⁻²). Individuals with more than 2 missing genotypesout of the 12 loci were also excluded from the analysis.

Haplotype-Based Association Tests:

These analyses were carried out in PLINK v1.07 [35]. Haplotypes werefirst phased using EM algorithm across the HLA-DQB1, HLA-DQA1, HLA-DRB1region. The haplotype frequencies were estimated in the cases andcontrols separately, as well as jointly in the entire cohort. Onlycommon haplotypes with overall frequency>1% were included in theassociation tests. Global haplotype association test was performed usinga χ² test with n−1 degrees of freedom for n common haplotype groups. TheORs and the corresponding 95% confidence intervals were estimated inreference to the most common haplotype (GCAT, frequency˜35%).

First-Order Interaction Analyses:

To explore the possibility of interactions between the 7 independentrisk variants, we screened all possible pairwise interaction terms forassociation with disease within the framework of logistic regressionmodels (R version 2.10). As a screening test, we used 1-df LRT tocompare two nested models: one with main effects only and one with maineffects and a multiplicative (logit-additive) interaction term. Weincluded cohort membership as a fixed covariate in both of these models.For this analysis we selected a Bonferroni-adjusted significance of2.4×10⁻³, a conservative threshold that accounts for all 21 pairwiseinteraction terms tested. Significant interactions from this analysiswere also tested using a 4-df genotypic interaction test. In this test,we compared a model with allelic effects, dominant effects, and theirinteraction terms with a reduced model with no interaction terms. Wefollowed the coding proposed by Cordell and Clayton: for each SNP i wemodeled its allelic effect x_(ia) by coding the genotypes AA, AB, and BBas x_(ia)=−1, 0, 1; we modeled dominance effects as x_(id)=−0.5, 0.5,−0.5 for the genotypes AA, AB, and BB, respectively [38].

Distributions of Protective Alleles and Risk Score Analyses:

Each study participant was scored for the number of risk alleles and thedistributions of protective alleles were compared between cohorts ofdifferent ethnicity. Only individuals with complete genotype informationat the 7 scored loci (14 alleles) were included in this analysis. Thedistributions were analyzed separately for cases and controls. A χ²goodness-of-fit test was used to derive p-values for comparison ofdistributions. Because of a relatively small number of individuals atthe tails of the distributions, for the purpose of statistical testingthe tails of the distributions were binned into single-bin categories toachieve expected cell counts>5.

To confirm the results of conditional analyses and refine the geneticrisk score proposed in the original GWAS, we subjected the genotype datafrom the entire cohort to a stepwise regression algorithm that selectssignificant covariates for the best predictive regression model based onBayesian Information Criterion (the step function, R version 2.10). Atmodel entry, we included all 12 genotyped SNPs, all 21 testedinteractions, as well as cohort membership as a fixed covariate.Consistent with the results of our conditional analysis, the stepwisealgorithm retained only the 7 SNPs exhibiting an independent effectalong with the rs6677604*rs2412971 interaction term. All other termswere automatically dropped from the regression model.

The risk score was calculated as a weighted sum of the number ofprotective alleles at each locus multiplied by the log of the OR foreach of the individual loci from the final fully adjusted model. Onlyindividuals with non-missing genotypes for all 14 alleles were includedin this analysis. The risk score was standardized across all populationsusing a z-score transformation, thus the standardized score representedthe distance between the raw score and the population mean in units ofstandard deviation. The percentage of the total variance in diseasestate explained by the risk score was estimated by Nagelkerke's pseudoR² from the logistic regression model with the risk score as aquantitative predictor and disease state as an outcome. The C-statisticwas estimated as an area under the receiver operating characteristiccurve provided by the above logistic model. These analyses were carriedout with SPSS Statistics version 19.0.

There were pronounced differences in the distributions of protectivealleles among the three different ethnicities studied, with Asiancontrols carrying the highest number of protective alleles andAfrican-Americans controls, the lowest. Global geographic variation inthe genetic risk for IgAN was studied by applying the newly refined IgANrisk score in 6,319 healthy individuals across 85 worldwide populations.Marked differences were seen in the genetic risk across the world.Overall, the mean standardized risk score was lowest for Africans,intermediate for Middle Easterners and Europeans, and highest for EastAsians and Native Americans.

TABLE 1 Summary of Study Cohorts Genotyped After QC Cohort EthnicityCases Controls Cases Controls Discovery Han Chinese 1,228 966 1,194 902Cohort Follow-up Han Chinese 740 750 712 748 Cohort 1 Follow-up European1,273 1,201 1,238 1,172 Cohort 2 All Cohorts Combined: 3,241 2,917 3,1442,822

TABLE 2 Association results for 10 SNPs representing 5 independentregions that reach genome-wide significance in combined analyses.Beijing Discovery Cohort^(a) Shanghai Replication Cohort^(a) N = 2,096 N= 1,460 (1,194 cases/902 controls) (712 cases/748 controls) Location SNPMAF (cases/ MAF (cases/ Chr (kb) (minor allele) controls) OR P-valuecontrols) OR P-value 1 194,918 rs3766404 (C) 0.052/0.086 0.59 1.84 ×10−5 0.078/0.080 0.98 8.18 × 10−1 1 194,953 rs6677604 (A) 0.041/0.0730.55 1.20 × 10−5 0.052/0.070 0.73 3.22 × 10−2 6 32,778 rs2856717 (T)0.19/0.26 0.66 3.31 × 10−8 0.14/0.20 0.69 1.51 × 10−4 6 32,789 rs9275596(C) 0.14/0.22 0.56 1.91 × 10−12 0.09/0.16 0.54 6.29 × 10−8 6 32,917rs9357155 (A) 0.15/0.20 0.69 5.19 × 10−6 0.12/0.18 0.64 1.79 × 10−5 632,919 rs2071543 (A) 0.16/0.22 0.70 7.19 × 10−6 0.14/0.20 0.65 1.59 ×10−5 6 33,194 rs1883414 (T) 0.19/0.24 0.73 3.26 × 10−5 0.17/0.20 0.823.55 × 10−2 6 33,205 rs3129269 (T) 0.21/0.27 0.73 1.32 × 10−5 0.20/0.230.83 3.48 × 10−2 22  28,824 rs2412971 (A) 0.31/0.39 0.72 8.21 × 10−70.24/0.28 0.83 2.79 × 10−2 22  28,859 rs2412973 (A) 0.32/0.39 0.73 1.91× 10−6 0.26/0.30 0.83 2.68 × 10−2 European Replication Cohort^(b) AllCohorts Combined^(b) N = 2,410 N = 5,966 (1,238 cases/1,172 controls)(3,144 cases/2,822 controls) Location SNP MAF (cases/ Per OR Chr (kb)(minor allele) controls) OR P-value allele Het. Hom. P-value Q 1 194,918rs3766404 (C) 0.12/0.14 0.82 1.46 × 10−2 0.77 0.79 0.45 4.24 × 10−5 0.01 194,953 rs6677604 (A) 0.17/0.23 0.71 1.19 × 10−5 0.68 0.69 0.41 2.96 ×10−10 0.1 6 32,778 rs2856717 (T) 0.28/0.33 0.77 3.32 × 10−6 0.73 0.690.59 8.44 × 10−16 0.44 6 32,789 rs9275596 (C) 0.20/0.27 0.70 7.40 ×10−10 0.63 0.62 0.43 1.59 × 10−26 0.31 6 32,917 rs9357155 (A) 0.11/0.130.77 8.26 × 10−4 0.71 0.66 0.62 2.11 × 10−12 0.35 6 32,919 rs2071543 (A)0.12/0.14 0.81 1.66 × 10−3 0.73 0.67 0.64 5.77 × 10−12 0.27 6 33,194rs1883414 (T) 0.29/0.33 0.82 2.17 × 10−4 0.78 0.77 0.61 4.84 × 10−9 0.556 33,205 rs3129269 (T) 0.33/0.38 0.83 6.67 × 10−4 0.79 0.79 0.61 8.54 ×10−9 0.42 22  28,824 rs2412971 (A) 0.46/0.51 0.82 1.61 × 10−3 0.80 0.750.66 1.86 × 10−9 0.29 22  28,859 rs2412973 (A) 0.46/0.51 0.83 2.09 ×10−3 0.80 0.76 0.66 4.46 × 10−9 0.28 ^(a)Cochran-Armitage trend test;^(b)Stratified analysis using Mantel's extension of Cochran-Armitagetrend test; Q: p-value for the Cochrane's Q statistic; *significantheterogeneity (P < 0.05). The per-allele, heterozygote and homozygoteOR's are indicated for the combined cohort.

TABLE 3 Stepwise conditional analysis of association among the signalsin the HLA region. All Cohorts Combined Beijing Discovery CohortShanghai Follow-up Cohort European Follow-up Cohort N = 5,966 N = 2,096N = 1,460 N = 2,410 (3,144 cases/2,822 controls) (1,194 cases/902controls) (712 cases/748 controls) (1,238 cases/1,172 controls) Con-Conditioning Unconditioned Conditioned Unconditioned ConditionedUnconditioned Conditioned Unconditioned ditioned Test SNP SNP(s) p-valuep-value p-value p-value p-value p-value p-value p-value rs2856717rs9275596 3.30 × 10−8 0.280 1.51 × 10−4 0.271 3.32 × 10−6 0.354 8.44 ×10−16 0.114 rs9275596 1.91 × 10−12 NA 6.29 × 10−8 NA 7.40 × 10−10 NA1.59 × 10−26 NA rs9357155 5.19 × 10−6 2.29 × 10−3 1.79 × 10−5 3.12 ×10−4 8.26 × 10−4 8.83 × 10−4 2.11 × 10−12 6.87 × 10−9 rs1883414 1.32 ×10−5 2.16 × 10−4 0.0348 0.164 6.67 × 10−4 3.64 × 10−4 8.54 × 10−9 9.94 ×10−8 rs2856717 rs9275596, 3.30 × 10−8 0.236 1.51 × 10−4 0.225 3.32 ×10−6 0.303 8.44 × 10−16 0.0754 rs9275596 rs9357155 1.91 × 10−12 NA 6.29× 10−8 NA 7.40 × 10−10 NA 1.59 × 10−26 NA rs9357155 5.19 × 10−6 NA 1.79× 10−5 NA 8.26 × 10−4 NA 2.11 × 10−12 NA rs1883414 1.32 × 10−5 7.04 ×10−5 0.0348 0.059 6.67 × 10−4 7.18 × 10−4 8.54 × 10−9 3.13 × 10−8rs2856717 rs9275596, 3.30 × 10−8 0.278 1.51 × 10−4 0.241 3.32 × 10−60.272 8.44 × 10−16 0.0760 rs9275596 rs9357155, 1.91 × 10−12 NA 6.29 ×10−8 NA 7.40 × 10−10 NA 1.59 × 10−26 NA rs9357155 rs1883414 5.19 × 10−6NA 1.79 × 10−5 NA 8.26 × 10−4 NA 2.11 × 10−12 NA rs1883414 1.32 × 10−5NA 0.0348 NA 6.67 × 10−4 NA 8.54 × 10−9 NA rs9275596 and rs2856717represent the major HLA signal near DQB1. rs9357155 and rs1883414represent the other two independent signals in the HLA region.

TABLE 4 Cumulative effect of replicated loci stratified by the number ofprotective alleles. Beijing Discovery Cohort (N = 2,074)* AsianReplication Cohort (N = 1,397)* European Replication Cohort (N = 2,160)*1,176 cases/898 controls 685 cases/712 controls 1,098 cases/1,062controls No. of Frequency Average Frequency Frequency Protective (Cases/Risk Score (Cases/ Average Risk (Cases/ Average Risk Alleles Controls)(+/−SD) OR (95% CI) Controls) Score (+/− SD) OR (95% CI) Controls) Score(+/− SD) OR (95% CI) 0 (highest 0.17/0.07 0.00 1.00 (reference)0.24/0.13 0.00 1.00 (reference) 0.07/0.03 0.00 1.00 risk) (refererence)1 0.31/0.26 −0.37 0.50 (0.36-0.69) 0.38/0.32 −0.30 0.66 (0.48-0.90)0.19/0.12 −0.11 (+/−0.04) 0.59 (0.36-0.97) (+/−0.09) (+/−0.15) 20.29/0.29 −0.77 0.40 (0.29-0.56) 0.24/0.31 −0.65 0.43 (0.31-0.60)0.26/0.24 −0.23 (+/−0.05) 0.39 (0.25-0.63) (+/−0.14) (+/−0.23) 30.16/0.20 −1.17 0.31 (0.22-0.44) 0.10/0.14 −1.06 0.40 (0.27-0.60)0.26/0.30 −0.35 (+/−0.06) 0.30 (0.19-0.48) (+/−0.15) (+/−0.26) 40.06/0.12 −1.61 0.20 (0.13-0.31) 0.04/0.08 −1.44 0.28 (0.16-0.47)0.15/0.19 −0.47 (+/−0.06) 0.28 (0.17-0.45) (+/−0.17) (+/−0.28) ≧5(lowest 0.01/0.06 −2.11 0.09 (0.05-0.16) 0.004/0.03 −1.86 0.10(0.03-0.33) 0.08/0.13 −0.65 (+/−0.10) 0.21 (0.12-0.35) risk) (+/−0.25)(+/−0.36) OR change 11.1 10.0 4.8 highest vs. lowest risk^(a)P-value^(b) 6.76 × 10⁻²⁷ 3.13 × 10⁻¹⁴ 6.24 × 10⁻¹⁷ C-stat 0.63(0.60-0.65) 0.61 (0.58-0.64) 0.60 (0.58-0.62) (95% CI)^(c) Nagelkerke0.072 0.054 0.042 R-sq^(d) *the risk scores were calculated based on theodds ratios and allele frequencies for each specific cohort Onlyindividuals with non-missing genotypes for all 10 alleles were includedin this analysis. ^(a)Fold-change in odds ratio between highest andlowest risk group. ^(b)P-value for the risk score prediction model.^(c)The C-statistic indicates the area under the receiver operatingcharacteristic (ROC) curve for the risk score prediction model.^(d)Nagelkerke's pseudo R² indicates the fraction of the variance inrisk explained by the risk score model.

TABLE 5 Replication Study Results and Combined Meta-analysis. Combinedassociation results for 12 SNPs representing 5 independent regions thatreached genome-wide significance in the original GWAS. The combinedeffect estimates (per allele odds ratios) in the replication cohortswere all direction-consistent with the ones in the original GWAScohorts. Significant heterogeneity was noted only for the second HLAlocus represented by rs9357155 and rs2071543. Replication and GWAS N =10,755 Replication Study across 12 cohorts N = 4,789 across 8 cohorts(5,372 cases/ (2,228 cases/2,561 controls) 5,383 controls) Location SNPFixed Effects Random Effects^(#) Fixed Effects Chr (kb) (minor allele)OR P-value OR P-value I² Q-test OR P-value 1 194,918 rs3766404 (C) 0.782.5 × 10⁻⁴ 0.78 4.2 × 10⁻⁴ 0% 0.84 (NS) 0.78 7.9 × 10⁻⁸ 1 194,953rs6677604 (A) 0.78 3.1 × 10⁻⁵ 0.78 5.5 × 10⁻⁵ 0% 0.48 (NS) 0.74 2.1 ×10⁻¹³ 6 32,768 rs9275224 (A) 0.75 3.6 × 10⁻¹¹ 0.75 7.1 × 10⁻¹¹ 0% 0.67(NS) 0.72 8.5 × 10⁻³⁰ 6 32,778 rs2856717 (T) 0.86 1.1 × 10⁻³ 0.86 1.8 ×10⁻³ 0% 0.71 (NS) 0.77 6.6 × 10⁻¹⁶ 6 32,779 rs9275424 (G) 1.22 5.0 ×10⁻⁵ 1.22 8.7 × 10⁻⁵ 19% 0.27 (NS) 1.28 2.6 × 10⁻¹⁴ 6 32,789 rs9275596(C) 0.75 5.3 × 10⁻⁹ 0.75 9.5 × 10⁻⁹ 0% 0.60 (NS) 0.67 5.0 × 10⁻³² 632,917 rs9357155 (A) 0.96 5.8 × 10⁻¹ 0.97 9.4 × 10⁻² 54% 0.025* 0.79 1.1× 10⁻⁸ 6 32,919 rs2071543 (A) 0.91 1.7 × 10⁻¹ 0.92 1.2 × 10⁻¹ 43% 0.08(NS) 0.78 5.7 × 10⁻¹⁰ 6 33,194 rs1883414 (T) 0.87 3.1 × 10⁻³ 0.87 5.0 ×10⁻³ 0% 0.96 (NS) 0.82 3.0 × 10⁻¹⁰ 6 33,205 rs3129269 (T) 0.89 1.1 ×10⁻² 0.89 1.7 × 10⁻² 0% 0.75 (NS) 0.83 2.5 × 10⁻⁹ 22  28,824 rs2412971(A) 0.81 1.1 × 10⁻⁶ 0.81 2.1 × 10⁻⁶ 24% 0.23 (NS) 0.80 4.0 × 10⁻¹⁵ 22 28,859 rs2412973 (A) 0.81 6.9 × 10⁻⁷ 0.81 1.2 × 10⁻⁶ 29% 0.19 (NS) 0.809.9 × 10⁻¹⁵ Replication and GWAS N = 10,755 across 12 cohorts (5,372cases/5,383 controls) Location SNP Random Effects^(#) Chr (kb) (minorallele) OR P-value I² Q-test Gene Annotation 1 194,918 rs3766404 (C)0.78 1.3 × 10⁻⁷ 6% 0.39 (NS) CFH, CFHR1, 1 194,953 rs6677604 (A) 0.744.6 × 10⁻¹³ 21% 0.23 (NS) CFHR3 6 32,768 rs9275224 (A) 0.72 2.8 × 10⁻²⁹0% 0.69 (NS) HLA-DQB1, -DQA1, 6 32,778 rs2856717 (T) 0.78 7.3 × 10⁻¹⁶29% 0.16 (NS) -DRB1 6 32,779 rs9275424 (G) 1.26 4.6 × 10⁻¹⁴ 30% 0.14(NS) 6 32,789 rs9275596 (C) 0.67 3.1 × 10⁻³² 43% 0.05 (NS) 6 32,917rs9357155 (A) 0.87 2.6 × 10⁻¹¹ 70% 1.0 × 10⁻⁴** HLA-DOB, PSMB8, 6 32,919rs2071543 (A) 0.84 4.0 × 10⁻¹¹ 61% 2.0 × 10⁻³** PSMB9, TAP1, TAP2 633,194 rs1883414 (T) 0.82 5.9 × 10⁻¹⁰ 0% 0.86 (NS) HLA-DPB2, -DPB1, 633,205 rs3129269 (T) 0.83 4.6 × 10⁻⁹ 0% 0.51 (NS) -DPA1 22  28,824rs2412971 (A) 0.80 9.5 × 10⁻¹⁵ 12% 0.33 (NS) HORMAD2, 22  28,859rs2412973 (A) 0.80 2.3 × 10⁻¹⁴ 16% 0.29 (NS) MTMR3, LIF, OSM, GATSL3,SF3A1 Q-test: P-value for the Cochrane's Q statistic for heterogeneity,NS: heterogeneity test not significant, *heterogeneity P < 0.05,**heterogeneity P < 0.01; I²: Heterogeneity Index (0-100%), where <25%corresponds to low, 50%-75% to medium, and >75% to high level ofheterogeneity; OR: Additive (per-allele) Odds Ratio; ^(#) Han and Eskinrandom effects model.

TABLE 6 Conditional analysis of the HLA-DQB1, HLA-DQA1, HLA-DRB1 locus.Replication Study Replication and GWAS N = 4,789 across 8 cohorts N =10,755 across 12 cohorts (2,228 cases/2,561 controls) (5,372 cases/5,383controls) UNADJUSTED CONDITIONED UNADJUSTED CONDITIONED OR P-value ORP-value OR P-value OR P-value CONDITIONING SNPs rs9275224 0.75 4 × 10⁻¹¹0.71 2 × 10⁻⁶ 0.72 9 × 10⁻³⁰ 0.75 7 × 10⁻¹⁰ rs2856717, rs9275424,rs9275596 rs2856717 0.86 1 × 10⁻³ 1.72 1 × 10⁻⁶ 0.77 7 × 10⁻¹⁶ 1.61 2 ×10⁻¹⁰ rs9275224, rs9275424, rs9275596 rs9275424 1.22 5 × 10⁻⁵ 1.06 3 ×10⁻¹ 1.28 3 × 10⁻¹⁴ 1.11 7 × 10⁻³ rs9275224, rs2856717, rs9275596rs9275596 0.75 5 × 10⁻⁹ 0.64 2 × 10⁻⁶ 0.67 5 × 10⁻³² 0.58 3 × 10⁻¹⁶rs9275224, rs2856717, rs9275424

TABLE 7 Haplotype analysis of rs9275224, rs2856717, rs9275424, andrs9275596 at the HLA-DQB1/DRB1 locus. The most common haplot of 4 majoralleles (GCAT) is used as a reference to derive odds ratios for allother haplotypes. Only common haplotypes (frequency >1%) are tested forassociation. All Cohorts: N = 10,755 Freq. Freq. Freq. Overall CasesControls OR 95% CI P-global GCAT 0.352 0.365 0.338 -reference--reference- ATAC 0.213 0.180 0.245 0.69 0.64-0.74 ACAT 0.130 0.119 0.1410.78 0.71-0.85 3 × 10⁻⁴³ ATAT 0.050 0.058 0.043 1.25 1.10-1.42 GCGT0.246 0.270 0.222 1.12 1.04-1.20

TABLE 8 The comparison of the original and the newly refined IgAN riskscore. Original Risk Score Newly Refined Risk Score Cohort: N^(#) R²*C** OR*** P-value**** R²* C** OR*** P-value**** Original GWAS Cohorts5,631 5.0% 0.61 1.51 3.1 × 10⁻⁴⁶ 5.7% 0.62 1.56 4.1 × 10⁻⁵² ReplicationCohorts 4,422 2.2% 0.58 1.29 5.4 × 10⁻¹⁷ 3.2% 0.59 1.36 3.3 × 10⁻²⁴Asian Cohorts Combined 4,582 4.5% 0.60 1.53 3.0 × 10⁻³⁴ 5.0% 0.61 1.522.6 × 10⁻³⁸ European Cohorts Combined 5,386 2.6% 0.58 1.34 3.7 × 10⁻²⁴3.6% 0.59 1.42 6.7 × 10⁻³³ All Cohorts Combined 10,053 3.8% 0.60 1.426.2 × 10⁻⁶³ 4.7% 0.61 1.47 1.2 × 10⁻⁷⁶ ^(#)Number of analyzedindividuals with 100% non-missing genotypes across all 7 scored loci.*R²: Nagelkerke R square (expressed as percentage) **C-statistic: areaunder the ROC curve ***odds ratio per one standard deviation of thestandardized risk score ****Wald's test for risk score as a quantitativepredictor of disease status.

TABLE 9 Case-control association results for the individual replicationcohorts. Italian French German Czech Cohort Cohort Cohort Cohort N =1,116 N = 895 N = 621 N = 465 (478 cases/638 (493 cases/402 (249cases/372 (244 cases/221 Loc SNP controls) controls) controls) controls)Chr (kb) (minor allele) OR P-value OR P-value OR P-value OR P-value 1194,918 rs3766404 (C) 0.85 2.0 × 10⁻¹ 0.72 6.4 × 10⁻² 0.71 3.8 × 10⁻²1.01 9.5 × 10⁻¹ 1 194,953 rs6677604 (A) 0.88 2.2 × 10⁻¹ 0.70 4.3 × 10⁻³0.74 3.5 × 10⁻² 1.02 9.2 × 10⁻¹ 6 32,768 rs9275224 (A) 0.78 6.4 × 10⁻³0.76 4.7 × 10⁻³ 0.71 4.4 × 10⁻³ 0.84 2.3 × 10⁻¹ 6 32,778 rs2856717 (T)0.91 3.5 × 10⁻¹ 0.89 2.7 × 10⁻¹ 0.87 2.6 × 10⁻¹ 0.89 3.6 × 10⁻¹ 6 32,779rs9275424 (G) 0.99 9.6 × 10⁻¹ 1.36 6.0 × 10⁻³ 1.20 1.8 × 10⁻¹ 1.21 2.5 ×10⁻¹ 6 32,789 rs9275596 (C) 0.78 2.1 × 10⁻² 0.82 6.5 × 10⁻² 0.82 1.2 ×10⁻¹ 0.73 6.7 × 10⁻² 6 32,917 rs9357155 (A) 0.76 5.9 × 10⁻² 1.23 1.6 ×10⁻¹ 0.71 6.0 × 10⁻² 0.79 2.4 × 10⁻¹ 6 32,919 rs2071543 (A) 0.82 1.6 ×10⁻¹ 1.11 4.7 × 10⁻¹ 0.67 2.8 × 10⁻² 0.71 1.1 × 10⁻¹ 6 33,194 rs1883414(T) 0.90 2.6 × 10⁻¹ 0.84 8.1 × 10⁻² 1.00 9.9 × 10⁻¹ 0.80 1.2 × 10⁻¹ 633,205 rs3129269 (T) 0.94 4.9 × 10⁻¹ 0.92 4.1 × 10⁻¹ 1.01 9.5 × 10⁻¹0.76 5.7 × 10⁻² 22  28,824 rs2412971 (A) 0.85 6.7 × 10⁻² 0.81 2.2 × 10⁻²0.86 1.8 × 10⁻¹ 0.92 5.1 × 10⁻¹ 22  28,859 rs2412973 (A) 0.85 5.8 × 10⁻²0.79 1.4 × 10⁻² 0.86 1.7 × 10⁻¹ 0.93 5.5 × 10⁻¹ African- HungarianChinese Japanese American Cohort Cohort Cohort Cohort N = 431 N = 617 N= 550 N = 94 (138 cases/293 (333 cases/284 (259 cases/291 (34 cases/60Loc SNP controls) controls) controls) controls) Chr (kb) (minor allele)OR P-value OR P-value OR P-value OR P-value 1 194,918 rs3766404 (C) 0.792.7 × 10⁻¹ 0.68 7.7 × 10⁻² 0.72 1.8 × 10⁻¹ 0.60 1.2 × 10⁻¹ 1 194,953rs6677604 (A) 0.73 9.4 × 10⁻² 0.66 8.6 × 10⁻² 0.71 2.6 × 10⁻¹ 0.59 1.1 ×10⁻¹ 6 32,768 rs9275224 (A) 0.60 9.5 × 10⁻⁴ 0.80 6.2 × 10⁻² 0.74 1.8 ×10⁻² 0.60 1.1 × 10⁻¹ 6 32,778 rs2856717 (T) 0.65 6.7 × 10⁻³ 0.94 6.5 ×10⁻¹ 0.76 9.7 × 10⁻² 0.71 3.5 × 10⁻¹ 6 32,779 rs9275424 (G) 1.37 9.6 ×10⁻² 1.14 4.1 × 10⁻¹ 1.47 2.6 × 10⁻³ 0.82 5.6 × 10⁻¹ 6 32,789 rs9275596(C) 0.58 1.2 × 10⁻³ 0.70 1.5 × 10⁻² 0.66 3.9 × 10⁻² 0.78 4.8 × 10⁻¹ 632,917 rs9357155 (A) 1.59 5.4 × 10⁻² 1.23 1.9 × 10⁻¹ 0.83 3.2 × 10⁻¹1.56 4.2 × 10⁻¹ 6 32,919 rs2071543 (A) 1.58 5.3 × 10⁻² 1.17 5.1 × 10⁻¹0.80 2.1 × 10⁻¹ 1.26 6.0 × 10⁻¹ 6 33,194 rs1883414 (T) 0.90 5.1 × 10⁻¹0.83 1.8 × 10⁻¹ 0.84 2.5 × 10⁻¹ 0.70 3.9 × 10⁻¹ 6 33,205 rs3129269 (T)0.96 7.9 × 10⁻¹ 0.82 1.5 × 10⁻¹ 0.74 7.2 × 10⁻² 0.74 4.4 × 10⁻¹ 22 28,824 rs2412971 (A) 0.97 8.4 × 10⁻¹ 0.72 9.5 × 10⁻³ 0.57 1.7 × 10⁻⁴0.62 1.3 × 10⁻¹ 22  28,859 rs2412973 (A) 0.98 8.9 × 10⁻¹ 0.72 9.8 × 10⁻³0.57 1.3 × 10⁴ 0.62 1.3 × 10⁻¹

TABLE 10 Pairwise LD between the SNPs of the HLA region: r2 (top righthalf) and D′ (bottom left half) for all cohorts (top), Europeans(middle) and Asians (bottom). r² D′ rs9275224 rs2856717 rs9275424rs9275596 rs9357155 rs1883414 All cohorts: N = 10,755 rs9275224 0.5430.212 0.387 0.000 0.014 rs2856717 0.998 0.117 0.715 0.004 0.001rs9275424 0.997 0.997 0.091 0.000 0.003 rs9275596 0.946 0.947 0.9930.002 0.001 rs9357155 0.049 0.101 0.013 0.066 0.001 rs1883414 0.1590.030 0.163 0.039 0.108 European Cohorts: N = 5,938 rs9275224 0.6230.205 0.451 0.005 0.000 rs2856717 0.998 0.130 0.715 0.000 0.002rs9275424 0.997 0.998 0.104 0.007 0.000 rs9275596 0.943 0.938 0.9940.002 0.002 rs9357155 0.229 0.041 0.127 0.206 0.003 rs1883414 0.0280.093 0.029 0.115 0.101 Asian Cohorts: N = 4,723 rs9275224 0.437 0.2170.300 0.003 0.050 rs2856717 0.997 0.096 0.699 0.037 0.004 rs92754240.998 1.000 0.072 0.010 0.008 rs9275596 0.950 0.959 0.995 0.039 0.008rs9357155 0.102 0.224 0.381 0.200 0.007 rs1883414 0.343 0.065 0.2860.105 0.381

TABLE 11 The best predictive model for IgAN based on all the genotypedSNPs and their pairwise interaction terms. This model represents thesolution of a stepwise logistic regression algorithm (BIC-based stepwisemodel selection). The coefficients from this model are used to refinethe risk score for IgAN. Predictor Best Predictive Model (ReferenceAllele) Coeficient (β) OR (95% CI) P-value Chr. Annotation of Genes inthe Region rs6677604 (A) −0.49371 0.61 (0.53-0.71) 2.2 × 10⁻¹¹ 1q32 CFH,CFHR1, CFHR3 rs9275224 (A) −0.31307 0.73 (0.67-0.80) 2.5 × 10⁻¹¹ 6p21HLA-DQB1, -DQA1, -DRB1 (variant 1) rs2856717 (T) 0.42265 1.53(1.31-1.78) 8.2 × 10⁻⁸  6p21 HLA-DQB1, -DQA1, -DRB1 (variant 2)rs9275596 (C) −0.51157 0.60 (0.52-0.69) 5.9 × 10⁻¹³ 6p21 HLA-DQB1,-DQA1, -DRB1 (variant 3) rs9357155 (A) −0.28621 0.75 (0.69-0.82) 3.8 ×10⁻¹⁰ 6p21 HLA-DOB, PSMB8, PSMB9, TAP1, TAP2 rs1883414 (T) −0.1805 0.83(0.78-0.90) 4.8 × 10⁻⁷  6p21 HLA-DPB2, -DPB1, -DPA1 rs2412971 (A)−0.28592 0.75 (0.70-0.81) 2.3 × 10⁻¹⁵ 22q12 HORMAD2, MTMR3, LIF, OSM,GATSL3, SF3A1 rs6677604 (A) * 0.23171 1.26 (1.12-1.43) 2.2 × 10⁻⁴  —1q32 by 22q12 interaction term rs2412971 (A)

TABLE 12 All possible 1^(st) order multiplicative interactions betweenthe 7 SNPs with independent effects on disease risk. Statisticalsignificance is assessed using a Bonferroni-corrected threshold, alpha0.05/21 = 2.4 × 10⁻³. p-value beta rs6677604 (A) rs9275224 (A) rs2856717(T) rs9275596 (C) rs9357155 (A) rs1883414 (T) rs2412971 (A) All cohorts:N = 10,755 rs6677604 (A) 0.80 (NS) 0.08 (NS) 0.13 (NS) 0.62 (NS) 0.89(NS) 2.5 × 10⁻⁴ *** rs9275224 (A) 0.01 0.26 (NS) 0.27 (NS) 0.52 (NS)0.40 (NS) 0.27 (NS) rs2856717 (T) 0.11 0.06 0.36 (NS) 0.90 (NS) 0.01(NS) 0.02 (NS) rs9275596 (C) 0.10 0.06 0.05 0.56 (NS) 0.01 (NS) 0.02(NS) rs9357155 (A) 0.04 0.04 0.01 −0.04   0.25 (NS) 0.09 (NS) rs1883414(T) −0.01 0.04 0.13 0.14 0.07 0.01 (NS) rs2412971 (A) 0.21 0.04 0.100.11 0.10 0.12 European Cohorts: N = 5,938 rs6677604 (A) 0.36 (NS) 0.16(NS) 0.48 (NS) 0.55 (NS) 0.30 (NS) 1.4 × 10⁻³ *** rs9275224 (A) 0.060.77 (NS) 0.96 (NS) 0.74 (NS) 0.37 (NS) 0.10 (NS) rs2856717 (T) 0.100.02 0.98 (NS) 0.98 (NS) 0.07 (NS) 0.16 (NS) rs9275596 (C) 0.05 0.000.00 0.59 (NS) 0.12 (NS) 0.39 (NS) rs9357155 (A) 0.06 0.03 0.00 −0.05  0.19 (NS) 0.04 (NS) rs1883414 (T) −0.08 0.05 0.11 0.10 0.12 0.06 (NS)rs2412971 (A) 0.22 0.09 0.08 0.05 0.17 0.11 Asian Cohorts: N = 4,723rs6677604 (A) 0.15 (NS) 0.71 (NS) 0.96 (NS) 0.23 (NS) 0.72 (NS) 0.60(NS) rs9275224 (A) −0.20 0.34 (NS) 0.31 (NS) 0.91 (NS) 0.58 (NS) 0.47(NS) rs2856717 (T) −0.06 0.09 0.75 (NS) 0.78 (NS) 0.24 (NS) 0.54 (NS)rs9275596 (C) −0.01 0.10 0.04 0.90 (NS) 0.07 (NS) 0.29 (NS) rs9357155(A) −0.23 0.01 −0.03   −0.01   0.96 (NS) 0.61 (NS) rs1883414 (T) 0.06−0.04   0.10 0.16 0.01 0.31 (NS) rs2412971 (A) 0.07 −0.05   0.05 0.09−0.04   0.08

TABLE 13 The comparison of the original and the newly refined geneticrisk score. Original Risk Score Newly Refined Risk Score Cohort N^(#)R²* C (95% CI)** OR (95% CI)*** P-value*** R²* C (95% CI)** OR (95%CI)*** P-value*** Italian Cohort 1,005 2.0% 0.57 (0.53-0.60) 1.30(1.14-1.49) 1.5 × 10⁻⁴ 3.4% 0.59 (0.56-0.63) 1.43 (1.24-1.64) 6.8 × 10⁻⁷French Cohort 859 1.8% 0.57 (0.53-0.61) 1.27 (1.10-1.45) 7.6 × 10⁻⁴ 2.8%0.58 (0.55-0.62) 1.36 (1.18-1.57) 2.6 × 10⁻⁵ German Cohort 571 2.3% 0.58(0.53-0.63) 1.35 (1.12-1.62) 1.9 × 10⁻³ 4.4% 0.60 (0.56-0.65) 1.54(1.26-1.88) 2.0 × 10⁻⁵ Czech Cohort 402 1.7% 0.57 (0.51-0.63) 1.23(1.03-1.46) 2.4 × 10⁻² 2.0% 0.57 (0.52-0.63) 1.23 (1.04-1.45) 1.5 × 10⁻²Hungarian Cohort 393 2.8% 0.59 (0.53-0.65) 1.40 (1.10-1.79) 5.7 × 10⁻³4.4% 0.61 (0.55-0.67) 1.54 (1.21-1.96) 5.1 × 10⁻⁴ Chinese Cohort 5951.6% 0.57 (0.52-0.62) 1.29 (1.07-1.57) 8.0 × 10⁻³ 2.7% 0.59 (0.54-0.63)1.36 (1.14-1.62) 6.2 × 10⁻⁴ Japanese Cohort 512 2.7% 0.59 (0.54-0.64)1.34 (1.12-1.60) 1.5 × 10⁻³ 4.0% 0.61 (0.56-0.65) 1.38 (1.17-1.62) 1.2 ×10⁻⁴ African-American 85 4.6% 0.63 (0.50-0.76) 1.50 (0.93-2.41) 9.6 ×10⁻² 5.1% 0.64 (0.51-0.77) 1.63 (0.94-2.82) 8.1 × 10⁻² Cohort AllReplication 4,422 2.2% 0.58 (0.56-0.59) 1.29 (1.22-1.37) 5.4 × 10⁻¹⁷3.2% 0.59 (0.57-0.61) 1.36 (1.28-1.45) 3.3 × 10⁻²⁴ Cohorts GWASDiscovery 2,091 7.0% 0.63 (0.60-0.65) 1.70 (1.54-1.88) 1.9 × 10⁻²⁴ 7.6%0.64 (0.61-0.66) 1.69 (1.54-1.86) 1.5 × 10⁻²⁶ GWAS Asian 1,384 5.3% 0.61(0.58-0.64) 1.65 (1.44-1.89) 5.8 × 10⁻¹³ 5.0% 0.61 (0.58-0.64) 1.57(1.39-1.78) 1.2 × 10⁻¹² Follow-up GWAS European 2,156 4.3% 0.60(0.58-0.63) 1.46 (1.34-1.60) 1.6 × 10⁻¹⁶ 5.3% 0.61 (0.59-0.64) 1.56(1.42-1.71) 1.0 × 10⁻¹⁹ Follow-up All GWAS Cohorts 5,631 5.0% 0.61(0.60-0.62) 1.51 (1.43-1.60) 3.1 × 10⁻⁴⁶ 5.7% 0.62 (0.60-0.63) 1.56(1.47-1.65) 4.1 × 10⁻⁵² All Asian Cohorts 4,582 4.5% 0.60 (0.59-0.62)1.53 (1.43-1.64) 3.0 × 10⁻³⁴ 5.0% 0.61 (0.59-0.63) 1.52 (1.43-1.62) 2.6× 10⁻³⁸ Combined All European 5,386 2.6% 0.58 (0.57-0.60) 1.34(1.26-1.41) 3.7 × 10⁻²⁴ 3.6% 0.59 (0.58-0.61) 1.42 (1.34-1.51) 6.7 ×10⁻³³ Cohorts Combined All Cohorts 10,053 3.8% 0.60 (0.59-0.61) 1.42(1.36-1.50) 6.2 × 10⁻⁶³ 4.7% 0.61 (0.60-0.62) 1.47 (1.42-1.54) 1.2 ×10⁻⁷⁶ Combined ^(#)Number of analyzed individuals with 100% non-missinggenotypes across all 7 scored loci. *R²: Nagelkerke R square expressedas percent **C-statistic: area under the ROC curve and its 95%confidence interval. ***odds ratio per one standard deviation of thestandardized risk score and its 95% confidence interval. ****Wald's testfor risk score as a quantitative predictor of disease status.

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What is claimed is:
 1. A method for treating IgA nephropathy (IgAN) in asubject having IgAN by reducing the expression of CFHR1 or CFHR3, orboth, comprising administering therapeutically effective amounts ofinhibitory oligonucleotides that reduce the expression of CFHR1 orCFHR3, or both to the subject, wherein the inhibitory oligonucleotidescomprise sufficient complementarity to the CFHR1 RNA or CFHR3 RNA tobind thereto under physiological conditions.
 2. The method of claim 1,wherein the inhibitory oligonucleotides are selected from the groupconsisting of siRNA, antisense, shRNA, microRNAs, microRNA mimetics, andribozymes.